Miracle-dz/UniFAR-FTUs huggingface dataset:
A neural network approach to ordinal regression

Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data mining tasks such as information retrieval, Web page ranking, collaborative filtering, and protein ranking in bioinformatics. The neural network software is available at: http://www.cs. missouri.edu/~chengji/cheng software.html.
Quantum error-correcting codes associated with graphs

We present a construction for quantum error correcting codes. The basic ingredients are a graph and a finite Abelian group, from which the code can explicitly be obtained. We prove necessary and sufficient conditions for the graph such that the resulting code corrects a certain number of errors. This allows a simple verification of the one-error correcting property of codes of length 5 in any dimension. As examples, we construct a large class of maximum distance separable codes, i.e. codes saturating the Singleton bound, as well as a code of length 10 detecting three errors.
Monte-Carlo Simulations of the First Passage Time for Multivariate Jump-Diffusion Processes in Financial Applications

Many problems in finance require the information on the first passage time (FPT) of a stochastic process. Mathematically, such problems are often reduced to the evaluation of the probability density of the time for such a process to cross a certain level, a boundary, or to enter a certain region. While in other areas of applications the FPT problem can often be solved analytically, in finance we usually have to resort to the application of numerical procedures, in particular when we deal with jump-diffusion stochastic processes (JDP). In this paper, we propose a Monte-Carlo-based methodology for the solution of the first passage time problem in the context of multivariate (and correlated) jump-diffusion processes. The developed technique provide an efficient tool for a number of applications, including credit risk and option pricing. We demonstrate its applicability to the analysis of the default rates and default correlations of several different, but correlated firms via a set of empirical data.
Moderating Effects of Scarcity on Consumer Traits and Motives towards Impulsive Buying Behaviour during Covid-19 Pandemic among Indonesian Millennials

—Research about impulsive buying behaviour has identified several determinant factors along the consumer journey, including psychological factors such as consumers’ traits and shopping motives. This study investigates the moderating impact of the scarcity created in flash-sale events on the linkage between consumers’ traits (sensation-seeking and self-identity) and shopping motives (hedonic and utilitarian). We collect 230 usable data using purposive sampling on e-marketplace consumers in Indonesia. We tested the hypothesis using Partial Least Square-Structural Equation Modeling (PLS-SEM). Direct effects of shopping motives and consumer traits toward impulse buying behaviour are evident in the result. Findings suggest scarcity moderates the linkage between utilitarian motive and sensation-seeking trait toward impulse buying behaviour, showing an enhancement effect. However, we find no support for moderating effect of scarcity on hedonic motive and self-identity trait. The uniqueness of the scarcity effect in creating impulse buying behaviour in the e-marketplace, can be used to develop strategies to increase sales in the e-marketplace.
Extending the Glucosyl Ceramide Cassette Approach: Application in the Total Synthesis of Ganglioside GalNAc-GM1b

The development of a novel cyclic glucosyl ceramide cassette acceptor for efficient glycolipid syntheses was investigated. p-Methoxybenzyl (PMB) groups were selected as protecting groups at C2 and C3 of the glucose residue with the aim of improving the functionality of the cassette acceptor. The choice of the PMB group resulted in a loss of β-selectivity, which was corrected by using an appropriate tether to control the spatial arrangement and the nitrile solvent effect. To investigate the effect of linker structure on the β-selectivity of intramolecular glycosylation, several linkers for tethering the glucose and ceramide moiety were designed and prepared, namely, succinyl, glutaryl, dimethylmalonyl, and phthaloyl esters. The succinyl ester linker was the best for accessing the cassette form. The newly designed glucosyl ceramide cassette acceptor was then applied in the total synthesis of ganglioside GalNAc-GM1b.
Uplink throughput in a single-macro cell/single-micro cell CDMA system, with application to data access points

Building upon the study of a single-macrocell/single-microcell CDMA system in (S. Kishore et al, IEEE Trans. on Wireless Comms., vol.2, no.2, p.364-374, 2003), we examine the performance of a specific example of such a two-tier system. In particular, the microcell base is converted into a data access point. Data access points (DAPs) are limited-range base stations that provide high-speed access to one user at a time. The microcell (or DAP) user operates on the same frequency as the macrocell users and has the same chip rate. However, it adapts its spreading factor (and thus its data rate) in accordance with interference conditions. By contrast, the macrocell serves multiple simultaneous data users, each with the same (fixed) rate. We analyze the achievable throughput for individual microcell users and present a simple, accurate approximation for its probability distribution. We also present average throughputs, both per-user and total. Our numerical results highlight the impact of a desensitivity parameter used in the base-selection process.
Soft tissue attachment structures on pyritized internal moulds of ammonoids

Paired and unpaired patterns can be recognized. Paired structures are observed along the umbilicus and on the flanks, and sometimes on the ventral side of the moulds. Unpaired ones appear on the dorsal and ventral sides. Paired double lines on the flanks of the phragmocone and of the body chamber, sometimes additionally connected with crescent shaped tracking bands, were left by lateral muscles. Small round or crescent shaped areas beneath each septum, sometimes connected with a tracking band were left by a ventral muscle. Small dark areas beneath each dorsal lobe were left by a dorsal muscle. Rounded black areas along the umbilicus, often connected with a dark tracking band are remains of a pair of dorsolateral muscles at the posterior end of the soft body. Black tracking bands connected to the incisions of the sutural lobes on the flanks of the phragmocone are interpreted as remains of muscles which fixed the posterior end of the soft body within the body chamber.
The combined technique for detection of artifacts in clinical electroencephalograms of sleeping newborns

In this paper, we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle, and noise artifacts and, as a consequence, some EEG features are irrelevant to classification problems. Combining the polynomial network and decision tree techniques, we discover comprehensible classification rules while also attempting to keep their classification error down. This technique is shown to outperform a number of commonly used machine learning technique applied to automatically recognize artifacts in the sleep EEGs.
Threshold values of random K‐SAT from the cavity method

Using the cavity equations of Mézard, Parisi, and Zecchina Science 297 (2002), 812 ; Mézard and Zecchina, Phys Rev E 66 (2002), 056126 we derive the various threshold values for the number of clauses per variable of the random K‐satisfiability problem, generalizing the previous results to K ≥ 4. We also give an analytic solution of the equations, and some closed expressions for these thresholds, in an expansion around large K. The stability of the solution is also computed. For any K, the satisfiability threshold is found to be in the stable region of the solution, which adds further credit to the conjecture that this computation gives the exact satisfiability threshold. © 2005 Wiley Periodicals, Inc. Random Struct. Alg. , 2006
No Internal Regret via Neighborhood Watch

We present an algorithm which attains O(\sqrt{T}) internal (and thus external) regret for finite games with partial monitoring under the local observability condition. Recently, this condition has been shown by (Bartok, Pal, and Szepesvari, 2011) to imply the O(\sqrt{T}) rate for partial monitoring games against an i.i. d. opponent, and the authors conjectured that the same holds for non-stochastic adversaries. Our result is in the affirmative, and it completes the characterization of possible rates for finite partial-monitoring games, an open question stated by (Cesa-Bianchi, Lugosi, and Stoltz, 2006). Our regret guarantees also hold for the more general model of partial monitoring with random signals.
Enhancing Content-And-Structure Information Retrieval using a Native XML Database

Three approaches to content-and-structure XML retrieval are analysed in this paper: first by using Zettair, a fulltext information retrieval system; second by using eXist, a native XML database, and third by using a hybrid XML retrieval system that uses eXist to produce the final answers from likely relevant articles retrieved by Zettair. INEX 2003 content-and-structure topics can be classified in two categories: the first retrieving full articles as final answers, and the second retrieving more specific elements within articles as final answers. We show that for both topic categories our initial hybrid system improves the retrieval effectiveness of a native XML database. For ranking the final answer elements, we propose and evaluate a novel retrieval model that utilises the structural relationships between the answer elements of a native XML database and retrieves Coherent Retrieval Elements. The final results of our experiments show that when the XML retrieval task focusses on highly relevant elements our hybrid XML retrieval system with the Coherent Retrieval Elements module is 1.8 times more effective than Zettair and 3 times more effective than eXist, and yields an effective content-and-structure XML retrieval.
Reducing DRAM Access Latency by Exploiting DRAM Leakage Characteristics and Common Access Patterns

DRAM-based memory is a critical factor that creates a bottleneck on the system performance since the processor speed largely outperforms the DRAM latency. In this thesis, we develop a low-cost mechanism, called ChargeCache, which enables faster access to recently-accessed rows in DRAM, with no modifications to DRAM chips. Our mechanism is based on the key observation that a recently-accessed row has more charge and thus the following access to the same row can be performed faster. To exploit this observation, we propose to track the addresses of recently-accessed rows in a table in the memory controller. If a later DRAM request hits in that table, the memory controller uses lower timing parameters, leading to reduced DRAM latency. Row addresses are removed from the table after a specified duration to ensure rows that have leaked too much charge are not accessed with lower latency. We evaluate ChargeCache on a wide variety of workloads and show that it provides significant performance and energy benefits for both single-core and multi-core systems.
Can Subcategorisation Probabilities Help a Statistical Parser

Research into the automatic acquisition of lexical information from corpora is starting to produce large-scale computational lexicons containing data on the relative frequencies of subcategorisation alternatives for individual verbal predicates. However, the empirical question of whether this type of frequency information can in practice improve the accuracy of a statistical parser has not yet been answered. In this paper we describe an experiment with a wide-coverage statistical grammar and parser for English and subcategorisation frequencies acquired from ten million words of text which shows that this information can significantly improve parse accuracy.
Effective Requirements Generation: Synchronizing Early Verification & Validation, Methods and Method Selection Criteria

This paper presents an approach for the implementation and execution of an effective requirements generation process. We achieve this goal by providing a well-defined requirements engineering model that includes verification and validation (V&V), and analysis. In addition, we identify focused activity objectives and map popular methods to lower-level activities, and define a criterion based process for optimizing method selection for attendant activities. Our model, unlike other models, addresses the complete requirements generation process and consists of activities defined at more adequate levels of abstraction. Furthermore, our model also incorporates a unique approach to V&V that enhances quality and reduces the cost of generating requirements. Additionally, activity objectives are identified and explicitly stated - not implied as in the current models. To assist in the selection of an appropriate set of methods, we have mapped commonly used methods to activities based on their objectives. Finally, we have identified method selection criteria and prescribed a reduced set of methods that optimize these criteria for each activity in our model. Thus, our approach assists in the task of selecting methods by using selection criteria to reduce a large collection of potential methods to a smaller, manageable set. The model, clear mapping of methods to activity objectives, and the criteria based process, taken together, provide the much needed guidance for the effective implementation and execution of the requirements generation process.
Users and Assessors in the Context of INEX: Are Relevance Dimensions Relevant?

main aspects of XML retrieval are identified by analysing and comparing the following two behaviours: the behaviour of the assessor when judging the relevance of returned doc- ument components; and the behaviour of users when inter- acting with components of XML documents. We argue that the two INEX relevance dimensions, Exhaustivity and Speci- ficity, are not orthogonal dimensions; indeed, an empirical analysis of each dimension reveals that the grades of the two dimensions are correlated to each other. By analysing the level of agreement between the assessor and the users, we aim at identifying the best units of retrieval. The results of our analysis show that the highest level of agreement is on highly relevant and on non-relevant document components, suggesting that only the end points of the INEX 10-point relevance scale are perceived in the same way by both the assessor and the users. We propose a new definition of rel- evance for XML retrieval and argue that its corresponding relevance scale would be a better choice for INEX.
On High Spatial Reuse Link Scheduling in STDMA Wireless Ad Hoc Networks

We consider the point-to-point link scheduling problem in Spatial Time Division Multiple Access (STDMA) wireless ad hoc networks, motivate the use of spatial reuse as performance metric and provide an explicit characterization of spatial reuse. We assume uniform transmission power at all nodes and propose an algorithm based on a graph model of the network as well as Signal to Interference and Noise Ratio (SINR) computations. Our algorithm achieves higher spatial reuse than existing algorithms, without compromising on computational complexity.
Space-efficient Routing Tables for Almost All Networks and the Incompressibility Method

We use the incompressibility method based on Kolmogorov complexity to determine the total number of bits of routing information for almost all network topologies. In most models for routing, for almost all labeled graphs, $\Theta (n^2)$ bits are necessary and sufficient for shortest path routing. By "almost all graphs" we mean the Kolmogorov random graphs which constitute a fraction of 1 - 1/nc of all graphs on n nodes, where c > 0 is an arbitrary fixed constant. There is a model for which the average case lower bound rises to $\Omega(n^2 \log n )$ and another model where the average case upper bound drops to $O(n \log^2 n)$. This clearly exposes the sensitivity of such bounds to the model under consideration. If paths have to be short, but need not be shortest (if the stretch factor may be larger than 1), then much less space is needed on average, even in the more demanding models. Full-information routing requires $\Theta (n^3)$ bits on average. For worst-case static networks we prove an $\Omega(n^2 \log n )$ lower bound for shortest path routing and all stretch factors < 2 in some networks where free relabeling is not allowed.
Labelled transition systems as a Stone space

A fully abstract and universal domain model for modal transition systems and refinement, developed in (27), is shown to be a maximal-points space model for the bisim- ulation quotient of labelled transition systems over a finite set of events. In this domain model we prove that this quotient is a Stone space whose compact, zero-dimensional, and ultra-metrizable Hausdorff topology measures the degree of bisimilarity and that image- finite labelled transition systems are dense. Using this compactness we show that the set of labelled transition systems that refine a modal transition system, its "set of implemen- tations," is compact and derive a compactness theorem for Hennessy-Milner logic on such implementation sets. These results extend to systems that also have partially specified state propositions, unify existing denotational, operational, and metric semantics on par- tial processes, render robust consistency measures for modal transition systems, and yield an abstract interpretation of compact sets of labelled transition systems as Scott-closed sets of modal transition systems.
On the security of the Yi-Tan-Siew chaotic cipher

This paper presents a comprehensive analysis on the security of the Yi-Tan-Siew chaotic cipher. A differential chosen-plaintext attack and a differential chosen-ciphertext attack are suggested to break the sub-key K, under the assumption that the time stamp can be altered by the attacker, which is reasonable in such attacks. Also, some security problems about the sub-keys /spl alpha/ and /spl beta/ are clarified, from both theoretical and experimental points of view. Further analysis shows that the security of this cipher is independent of the use of the chaotic tent map, once the sub-key K is removed via the proposed suggested differential chosen-plaintext attack.
Modeling informational novelty in a conversational system with a hybrid statistical and grammar-based approach to natural language generation

We present a hybrid statistical and grammar-based system for surface natural language generation (NLG) that uses grammar rules, conditions on using those grammar rules, and corpus statistics to determine the word order. We also describe how this surface NLG module is implemented in a prototype conversational system, and how it attempts to model informational novelty by varying the word order. Using a combination of rules and statistical information, the conversational system expresses the novel information differently than the given information, based on the run-time dialog state. We also discuss our plans for evaluating the generation strategy.
Preconditioned temporal difference learning

This paper extends many of the recent popular policy evaluation algorithms to a generalized framework that includes least-squares temporal difference (LSTD) learning, least-squares policy evaluation (LSPE) and a variant of incremental LSTD (iLSTD). The basis of this extension is a preconditioning technique that solves a stochastic model equation. This paper also studies three significant issues of the new framework: it presents a new rule of step-size that can be computed online, provides an iterative way to apply preconditioning, and reduces the complexity of related algorithms to near that of temporal difference (TD) learning.
Clustering by compression

We present a new method for clustering based on compression. The method does not use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the price of using the noncomputable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (ternary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics, we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.
Lessons learned from the deployment of a high-interaction honeypot

This paper presents an experimental study and the lessons learned from the observation of the attackers when logged on a compromised machine. The results are based on a six months period during which a controlled experiment has been run with a high interaction honeypot. We correlate our findings with those obtained with a worldwide distributed system of low-interaction honeypots
Distributed Source Coding Using Continuous-Valued Syndromes

This paper addresses the problem of coding a continuous random source correlated with another source which is only available at the decoder. The proposed approach is based on the extension of the channel coding concept of syndrome from the discrete into the continuous domain. If the correlation between the sources can be described by an additive Gaussian backward channel and capacity-achieving linear codes are employed, it is shown that the performance of the system is asymptotically close to the Wyner-Ziv bound. Even if such an additive channel is not Gaussian, the design procedure can fit the desired correlation and transmission rate. Experiments based on trellis-coded quantization show that the proposed system achieves a performance within 3-4 dB of the theoretical bound in the 0.5-3 bit/sample rate range for any Gaussian correlation, with a reasonable computational complexity.