Moisture Distribution and Dewatering Efficiency for Wet Materials

This review paper summarizes current research efforts toward a comprehensive view of wet material dewatering, considering the binding energy as the strength to hold water, and rupture energy given to remove moisture from materials.     

Perceiving,learning, and exploiting object affordances for autonomous pile manipulation

Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the most rudimentary manipulation task—such as removing objects from a pile—remains challenging for robots. We identify three major challenges that must be addressed to enable autonomous manipulation: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown man-made or natural objects are cluttered together in a pile. We present a system capable of manipulating unknown objects in such an environment. Our robot is tasked with clearing a table by removing objects from a pile and placing them into a bin. To that end, we address the three aforementioned challenges. Our robot perceives the environment with an RGB-D sensor, segmenting the pile into object hypotheses using non-parametric surface models. Our system then computes the affordances of each object, and selects the best affordance and its associated action to execute. Finally, our robot instantiates the proper compliant motion primitive to safely execute the desired action. For efficient and reliable action selection, we developed a framework for supervised learning of manipulation expertise. To verify the performance of our system, we conducted dozens of trials and report on several hours of experiments involving more than 1,500 interactions. The results show that our learning-based approach for pile manipulation outperforms a common sense heuristic as well as a random strategy, and is on par with human action selection.     

A semantic parsing approach for Bhutanese language of Dzongkha

Developments in the computational analysis of Dzongkha have been limited due to the syntactic complexity of the language. Though the natural language processing domains have witnessed rapid developments over the past decade; very few works has been done in Dzongkha despite of being the national language of Bhutan. In this paper, we have investigated the major problems in Dzongkha processing and have proposed a semantic parsing approach for effective processing of this language. We have used a probabilistic approach and have used the linguistic rules in Dzongkha to remove the ambiguities. Semantic representations along with belief net concepts have been used to increase the accuracy of segmentation, syntactic and semantic analyses. The proposed frame work has been able to solve the major issues related to Dzongkha processing, however needs to be further improved to include all the syntactic variations.     

Hardness and Fracture Toughness of SiC-Particle-Reinforced MoSi2 Composites

The indentation technique has been used to evaluate the hardness and fracture toughness of SiC-reinforced MoSi₂ composites made by hot-pressing. It is seen that the toughness increases with increasing indentation crack length (under increasing load) and a probable mechanism responsible for this behavior is described. It is also observed that there is an optimum volume fraction of SiC particles for which the maximum fracture toughness of the composite can be achieved.     

The effect of yttrium and thorium on the oxidation behavior of Ni-Cr-Al alloys

The effect of quaternary additions of 0.5% Y and 0.5 and 1.0% Th to a base alloy of Ni-10Cr-5Al on the oxidation behavior and mechanism was studied during oxidation in air over the range of 1000–1200°C. The presence of yttrium decreased the oxidation kinetics slightly, whereas the addition of thorium caused a slight increase. Oxide scale adherence was markedly improved by the addition of the quaternary elements. Although a number of oxides formed on yttrium-containing alloys, quantitative x-ray diffraction clearly showed that the rate-controlling step was the diffusion of oxygen through short-circuit paths in a thin layer of alumina that formed parabolically with time. Mixed oxides containing both aluminum and yttrium formed by the reaction of Y₂O₃ to form YAlOP₃ initially, and Y₃Al₅O₁₂ (YAG) after longer times. Although the scale adherence of the yttrium-containing alloy was considerably better than the base alloys, spalling did occur that was attributed to the formation of the voluminous YAG particles that grew in a mushroom-like manner, lifting the protective scale off the substrate locally. The YAG particles formed primarily at grain boundaries in the substrate in which the yttrium originally existed as YNi₉. This intermetallic compound reacted to form Y₂O₃, liberating metallic nickel that subsequently reacted to form NiO or NiAl₂O₄ spinel or both. The Y₂O₃ reacted with aluminum to ultimately form the YAG mushrooms. Thorium did not form any mixed oxides; the only oxide involving thorium was ThO₂, which existed as small particles at the oxide-metal interface. A highly beneficial effect of the thoria particles in reducing film spalling was observed. Scale spalling in the base alloy was attributed to void formation at the oxide-metal interface, the voids forming by condensation of excess vacancies from the Kirkendall effect associated with fast back-diffusion, of nickel into the substrate as aluminum was preferentially oxidized and diffused slowly outward. The mechanism of improved scale adherence in the quaternary alloys was the elimination of voids by annihilation of the Kirkendall vacancies at vacancy sinks introduced by the noncoherent interfaces between yttrium and thorium-containing intermetallics or oxides or both.This work is based on a portion of the dissertation of Arun Kumar in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering, University of California, Los Angeles.Supported by NASA-Ames under grant No. NGR 05-007-352.     

VILO: a rapid learning nearest-neighbor classifier for malware triage

VILO is a lazy learner system designed for malware classification and triage. It implements a nearest neighbor (NN) algorithm with similarities computed over Term Frequency $\times $ Inverse Document Frequency (TFIDF) weighted opcode mnemonic permutation features (N-perms). Being an NN-classifier, VILO makes minimal structural assumptions about class boundaries, and thus is well suited for the constantly changing malware population. This paper presents an extensive study of application of VILO in malware analysis. Our experiments demonstrate that (a) VILO is a rapid learner of malware families, i.e., VILO’s learning curve stabilizes at high accuracies quickly (training on less than 20 variants per family is sufficient); (b) similarity scores derived from TDIDF weighted features should primarily be treated as ordinal measurements; and (c) VILO with N-perm feature vectors outperforms traditional N-gram feature vectors when used to classify real-world malware into their respective families.     

Improving the performance of speaker and language identification tasks using unique characteristics of a class

In classification tasks, the error rate is proportional to the commonality among classes. In conventional GMM-based modeling technique, since the model parameters of a class are estimated without considering other classes in the system, features that are common across various classes may also be captured, along with unique features. This paper proposes to use unique characteristics of a class at the feature-level and at the phoneme-level, separately, to improve the classification accuracy. At the feature-level, the performance of a classifier has been analyzed by capturing the unique features while modeling, and removing common feature vectors during classification. Experiments were conducted on speaker identification task, using speech data of 40 female speakers from NTIMIT corpus, and on a language identification task, using speech data of two languages (English and French) from OGI_MLTS corpus. At the phoneme-level, performance of a classifier has been analyzed by identifying a subset of phonemes, which are unique to a speaker with respect to his/her closely resembling speaker, in the acoustic sense, on a speaker identification task. In both the cases (feature-level and phoneme-level) considerable improvement in classification accuracy is observed over conventional GMM-based classifiers in the above mentioned tasks. Among the three experimental setup, speaker identification task using unique phonemes shows as high as 9.56 % performance improvement over conventional GMM-based classifier.     

Two-stage database intrusion detection by combining multiple evidence and belief update

Insider threats have gained prominence and pose the most challenging threats to a database system. In this paper, we have proposed a new approach for detecting intrusive attacks in databases by fusion of information sources and use of belief update. In database intrusion detection, only intra-transactional features are not sufficient for detecting attackers within the organization as they are potentially familiar with the day-to-day work. Thus, the proposed system uses inter-transactional as well as intra-transactional features for intrusion detection. Moreover, we have also considered three different sensitivity levels of table attributes for keeping track of the malicious modification of the highly sensitive attributes more carefully. We have analyzed the performance of the proposed database intrusion detection system using stochastic models. Our system performs significantly better compared to two intrusion detection systems recently proposed in the literature.     

BLAST-SSAHA Hybridization for Credit Card Fraud Detection

A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.