Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Use of LSH for Privacy Preserving Personalization

The Locality Sensitive Hashing (LSH) technique of scalably finding nearest-neighbors can be adapted to enable discovering similar users while preserving their privacy. The key idea is to compute the user profile on the end-user device, apply LSH on the local profile, and use the LSH cluster identifier as the interest group identifier of a user. By properties of LSH, the interest group comprises other users with similar interests. The collective behavior of the members of the interest group is anonymously collected at some aggregation node to generate recommendations for the group members. The quality of recommendation depends on the efficiency of the LSH clustering algorithm, i.e. its capability of gathering similar users. In contrast, with conventional usage of LSH (for scalability and not privacy), in our framework one can not perform a linear search over the cluster members to identify the nearest neighbors and to prune away false positives. A good clustering quality is therefore of functional importance for our system. We report in this work how changing the nature of LSH inputs, which in our case corresponds to the user profile representations, impacts the performance of LSH-based clustering and the final quality of recommendations. We present extensive performance evaluations of the LSH-based privacypreserving recommender system using two large datasets of MovieLens ratings and Delicious bookmarks, respectively.
Rapid Dynamic Changes of Dendritic Inhibition in the Dentate Gyrus by Presynaptic Activity Patterns

The dentate gyrus (DG) serves as a primary gate to control information transfer from the cortex to the hippocampus. Activation of incoming cortical inputs results in rapid synaptic excitation followed by slow GABA-mediated (GABAergic) synaptic inhibition onto DG granule cells (GCs). GABAergic inhibitory interneurons (INs) in the DG comprise fast-spiking (FS) and non–fast-spiking (non-FS) cells. Anatomical analyses of DG INs reveal that FS cells are soma-targeting INs, whereas non-FS cells are dendrite-targeting INs. These two IN classes are differentially recruited by excitatory inputs and in turn provide exquisite spatiotemporal control over GC activity. Yet, little is known how FS and non-FS cells transform their presynaptic dynamics into varying postsynaptic response amplitudes. Using paired recordings in rat hippocampal slices, we show that inhibition in the DG is dominated by somatic GABAergic inputs during periods of sparse presynaptic activity, whereas dendritic GABAergic inputs are rapidly shifted to powerful and sustained inhibition during periods of intense presynaptic activity. The variant dynamics of dendritic inhibition is dependent on presynaptic IN subtypes and their activity patterns and is attributed to Ca2+-dependent increases in the probability of release and the size of the readily releasable pool. Furthermore, the degree of dynamic GABA release can be reduced by blocking voltage-gated K+ channels, which increases the efficacy of dendrite-targeting IN output synapses during sparse firing. Such rapid dynamic modulation of dendritic inhibition may act as a frequency-dependent filter to prevent overexcitation of GC dendrites and thus set the excitatory–inhibitory synaptic balance in the DG circuits.
Analysis and Evaluation of I/O Hypervisor Scheduling

Hypervisors' smooth operation and efficient performance has an immediate effect in the supported Cloud services. We investigate scheduling algorithms that match I/O requests originated from virtual resources, to the physical CPUs that do the actual processing. We envisage a new paradigm of virtualized resource consolidation, where I/O resources required by several Virtual Machines (VMs) in different physical hosts, are provided by one (or more) external powerful dedicated appliance(s), namely the I/O Hypervisor (IOH). For this reason I/O operations are transferred from the VMs to the IOH, where they are executed. We propose and evaluate a number of scheduling algorithms for this hypervisor model, concentrating on providing guaranteed fairness among the virtual resources. A simulator has been built that describes this model and is used for the implementation and the evaluation of the algorithms. We also analyze the performance of the different hypervisor models and highlight the importance of fair scheduling.
Kinetically resolved states of the Halobacterium halobium flagellar motor switch and modulation of the switch by sensory rhodopsin I

Spontaneous switching of the rotation sense of the flagellar motor of the archaebacterium Halobacterium halobium and modulation of the switch by attractant and repellent photostimuli were analyzed by using a computerized cell-tracking system with 67-ms resolution coupled to electronic shutters. The data fit a three-state model of the switch, in which a Poisson process governs the transition from state N (nonreversing) to state R (reversing). After a reversal, the switch returns to state N, passing through an intermediate state I (inactive), which produces a ca. 2-s period of low reversal frequency before the state N Poisson rate is restored. The stochastic nature of the H. halobium switch reveals a close similarity to Escherichia coli flagellar motor properties as elucidated previously. Sensory modulation of the switch by both photoattractant and photorepellent signals can be interpreted in terms of modulation of the single forward rate constant of the N to R transition. Insight into the mechanism of modulation by the phototaxis receptor sensory rhodopsin I (SR-I) was gained by increasing the lifetime of the principal photointermediate of the SR-I photochemical reaction cycle, S373, by replacing the native chromophore, all-trans-retinal, with the acyclic analog, 3,7,11-trimethyl-2,4,6,8-dodecapentaenal. Flash photolysis of analog-containing cells revealed an eightfold decrease in the rate of thermal decay of S373, and behavioral analysis showed longer periods of reversal suppression than that of cells with the native chromophore over similar ranges of illumination intensities. This indicates that attractant signaling is governed by the lifetime of the S373 intermediate rather than by the frequency of photocycling. In this sense, SR-I is similar to rhodopsin, whose function depends on an active photoproduct (Meta-II).
Resolution for a Temporal Logic of Robustness ( Extended Version )

The logic RoCTL* is an extension of the branching time temporal logic CTL* to represent robustness and reliability in systems. New operators are introduced dealing with obligation (where no failures occur) and robustness (where at most one additional failure occurs). The only known decision procedure for the temporal logic of robustness RoCTL* involves a reduction to the non-elementary QCTL* logic. Here we propose a CTL like restriction of RoCTL*, termed RoCTL, and show that this logic is useful in specifying systems. We also provide a satisfiability preserving translation into a normal form for CTL. By applying a known resolution calculus to the resulting normal form we obtain a resolution calculus for RoCTL and show that the complexity of satisfiability of RoCTL is EXPTIME. Unlike CTL, the RoCTL language includes non-state formulae. The translation deals with such formulae by marking the required path context with new propositions and passing this to subformulae. Hence we obtain a resolution based decision procedure for a branching time logic that contains non-state formulae.
Don&#146;t be a Pessimist: Use Snapshot based Concurrency Control for XML

As native XML database systems (e.g., [3, 7, 8]) get increasingly popular, fine-granularity concurrency control becomes imperative in order to allow different clients to concurrently access the same documents. Existing concurrency control approaches for XML are mainly based on locking [2, 3, 4, 6, 5]. However, the experiments of [5] have shown that the locking overhead, especially for read operations, can be tremendous. In this paper, we present two snapshot based concurrency control mechanisms that avoid locking. Instead, transactions access a committed snapshot of the data.
Methanol Extract of Codonopsis pilosula Inhibits Inducible Nitric Oxide Synthase and Protein Oxidation in Lipopolysaccharide-Stimulated Raw Cells

Purpose: To evaluate the mechanism of antioxidant activity of the methanol extract of Codonopsis pilosula. Methods: Anti-oxidative properties were assessed by measuring free radical scavenging activity, nitric oxide (NO) levels, protein oxidation and reducing power, while the mechanism of antioxidative effect of Codonopsis pilosula extract was determined by measuring iNOS and COX-2 expression in lipopolysaccharide (LPS)-stimulated raw cells. Results: Codonopsis pilosula extract (CPE) exerted significant DPPH free radical and NO-scavenging activities. Protein oxidation was decreased by 30 % by the CPE (1mg/ml). The extract (1mg/ml) enhanced reducing power 16-fold (compared with control). LPS-induced production of iNOS was significantly inhibited by the extract (60 %), suggesting that it inhibits NO production by suppressing iNOS expression. However, LPS-induced production of COX-2 was not significantly (p < 0.05) inhibited by the extract. The levels of total phenolics, total flavonoids, and total anthocyanin in CPE were 0.38, 2.10, and 2.47 mg/g, respectively. Conclusion: These results suggest that CPE exerts marked antioxidant activity via inhibition of iNOS and protein oxidation.
Relation detection between named entities: report of a shared task

In this paper we describe the first evaluation contest (track) for Portuguese whose goal was to detect and classify relations between named entities in running text, called ReRelEM. Given a collection annotated with named entities belonging to ten different semantic categories, we marked all relationships between them within each document. We used the following fourfold relationship classification: identity, included-in, located-in, and other (which was later on explicitly detailed into twenty different relations). We provide a quantitative description of this evaluation resource, as well as describe the evaluation architecture and summarize the results of the participating systems in the track.
Antifungal attributes of siderophore produced by the Pseudomonas aeruginosa JAS‐25

Siderophores produced by Pseudomonas sp. play a vital role in controlling several plant diseases. Public concern about chemical pesticides has fostered an interest to seek alternative control strategies. Production of siderophore by Pseudomonas aeruginosa JAS‐25 in King B medium was estimated at 400 nm and was partially purified by gel filtration chromatography using Sephadex G 25. Antifungal activity and inhibition of spore germination was studied against the phytopathogens like Fusarium oxysporum f. sp. ciceri, Fusarium udum, and Aspergillus niger by well plate assay and microphotographic method. Zone of inhibition of 3.0 cm was observed for F. udum and A. niger, proving it as potential biocontrol agent against phytopathogens. Partial 16S rRNA sequence analysis showed that the isolate JAS‐25 exhibited 99% homology with P. aeruginosa and could be of novel species and the sequence of the strain was deposited in Genbank with the accession number JX104229.
A Survey on Paraphrase Recognition

Paraphrase Recognition is a task of growing interest in the natural language process (NLP) research during the last years. This task aims to detect if two sentences have the same meaning. Paraphrase relationship described in this work is not the definition given by the common knowledge, but is more natural language oriented since it is driven by a lot of background human knowledge. This type of relation can be used as a support for many other NLP applications, such as Question Answering, Multi-document Summarization and for Machine Translation too. This paper presents an overview of the different phenomena that lead to paraphrase, of the methods used for Paraphrase Recognition and of different data-sets used for the task evaluation and of the main issue still opened.
The Interpretation of Pueblo Culture: A Question of Values

T CAN BE TAKEN as a general rule that intensive research upon the same preliterate people by a variety of ethnologists gives rise to considerable controversy and disagreement over the nature of fundamental institutions and cultural expressions. Things which are accepted verbatim about groups reported on by single field workers are subjected to considerable scrutiny and argument when the area is opened up to additional members of the profession. Studies of Pueblo Indians-especially the Hopi and Zunii-are instances of the operation of this rule. Controversies of long-standing over the relative definitions and composition of lineages, clans, and phratries are common; certain types of marriage, particularly cross-cousin marriage, have been equally debated; the kinship system has furnished considerable fuel for the argument over the relationship of terminology and social features. On the whole, such disputes are highly technical and concern only the inner circle of specialists. In most cases they have no great significance for wider philosophical and theoretical issues in social science. A controversy over somewhat different issues and having a more direct importance for broader problems will be discussed in this paper. In a series of publications appearing during the past few yearsl two principal interpretations 1 See references throughout the footnotes of this article for the most important papers. In addition a partial bibliography of equally relevant articles may be cited here: R. Bunzel, Introduction to Zuni Ceremonialism (Forty-seventh Annual Report, Bureau of American Ethnology, pp. 467-544, 1932). W. Dennis, The Hopi Child (New York, 1940). A. E. Kennard, Hopi Reactions to Death (American Anthropologist, vol. 39, pp. 491-496, 1937). K. A. Wittfogel and E. S. Goldfrank, Some Aspects of Pueblo Mythology and Society (Journal of American Folklore, vol. 56, pp. 17-30, 1943).
Why non-photorealistic rendering?

Since computer graphics' inception, programmers and artisans in computer animation have sought increasing realism in rendering, surface representation and movement. The "holy grai l" of computer animation would appear, to an outside observer, to be an image indistinguishable from the reality we all experience. For many years, I t ook this as my own quest, energized by the newness of the whole idea of computer animat ion, par t icu lar ly approaching it from the low end world of the Amiga, where anyone wi th a few bucks in the i r pocket could part ic ipate in the 3D computer animation world.
Hybrid model predictive control tracking of a sawtooth driving profile for an HEV

Based on a two-mode operation model of a parallel hybrid electric vehicle (PHEV) developed in previous work, this paper presents a hybrid optimal control solution for the power management problem of a PHEV. The optimal power flows between the vehicle's main subsystems are computed as solutions of a switching-system optimization (nonlinear programming problem) formulated at the supervisory level assuming a hierarchical vehicle control structure. The resulting equations are numerically solved using collocation methods. The approach is illustrated using the optimal and MPC tracking of a sawtooth velocity driving profile. Unlike other work, the assumption that there is always sufficient power to achieve perfect tracking is not made in this work.
Changes in the Influence of Alcohol-Paired Stimuli on Alcohol Seeking across Extended Training

Previous work has demonstrated that goal-directed control of alcohol-seeking and other drug-related behaviors is reduced following extended self-administration and drug exposure. Here, we examined how the magnitude of stimulus influences on responding changes across similar training and drug exposure. Rats self-administered alcohol or sucrose for 2 or 8 weeks. Previous work has shown that 8 weeks, but not 2 weeks of self-administration produces habitual alcohol seeking. Next, all animals received equivalent Pavlovian conditioning sessions where a discrete stimulus predicted the delivery of alcohol or sucrose. Finally, the impact of the stimuli on ongoing instrumental responding was examined in a Pavlovian–instrumental transfer (PIT) test. While a significant PIT effect was observed following 2 weeks of either alcohol or sucrose self-administration, the magnitude of this effect was greater following 8 weeks of training. The specificity of the PIT effect appeared unchanged by extended training. While it is well established that evaluation of the outcome of responding contributes less to behavioral control following extended training and/or drug exposure, our data indicate that reward–predictive stimuli have a stronger contribution to responding after extended training. Together, these findings provide insight into the factors that control behavior after extended drug use, which will be important for developing effective methods for controlling and ideally reducing these behaviors.
Learning with Shared Information for Image and Video Analysis

Image and video recognition is a fundamental and challenging problem in computer vision, which has progressed tremendously fast recently. In the real world, a realistic setting for image or video recognition is that we have some classes containing lots of training data and many classes that contain only a small amount of training data. Therefore, how to use the frequent classes to help learning the rare classes is an open question. Learning with shared information is an emerging topic which can solve this problem. There are different components that can be shared during concept modeling and machine learning procedure, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. For example, representations based on attributes define a finite vocabulary that is common to all categories, with each category using a subset of the attributes. Therefore, sharing some common attributes for multiple classes will benefit the final recognition system. In this thesis, we investigate some challenging image and video recognition problems under the framework of learning with shared information. My Ph.D research comprised of two parts. The first part focuses on the two domains (source and target) problems where the emphasis is to boost the recognition performance on the target domain by utilizing useful knowledge from source domain. The second part focuses on multi-domains problems where all domains are considered equally important. This means we want to improve performance for all domains by exploring the useful information across domains. In particular, we investigate three topics to achieve this goal in the thesis, which are active domain adaptation, multi-task learning, and dictionary learning, respectively.
A multisite comparison of actuarial risk instruments for sex offenders.

Four actuarial instruments for the prediction of violent and sexual reoffending (the Violence Risk Appraisal Guide [VRAG], Sex Offender Risk Appraisal Guide [SORAG], Rapid Risk Assessment for Sex Offender Recidivism [RRASOR] and Static-99) were evaluated in 4 samples of sex offenders (N = 396). Although all 4 instruments predicted violent (including sexual) recidivism and recidivism known to be sexually motivated, areas under the receiver operating characteristic (ROC) were consistently higher for the VRAG and the SORAG. The instruments performed better when there were fewer missing items and follow-up time was fixed, with an ROC area up to . 84 for the VRAG, for example, under such favorable conditions. Predictive accuracy was higher for child molesters than for rapists, especially for the Static-99 and the RRASOR. Consistent with past research, survival analyses revealed that those offenders high in both psychopathy and sexual deviance were an especially high-risk group.
Event Entailment Extraction Based on EM Iteration

In the research and development of various natural language processing systems, like Q&A system and text-to-scene conversation system, we realize that knowledge of text entailment helps a lot in improving the performance of the system. Systems with text entailment knowledge will be smarter than those who without entailment knowledge. Currently many research teams are focusing on text entailment, including recognition, generation and extraction. However, entailment extraction is the main method in creating entailment knowledge database. Meanwhile, for text-to-scene conversation system, due to the importance of events in stories, find a method for event entailment extraction is our main goal. In this paper, we proposed a method for extracting event entailment from corpus based on EM iteration, which has not been used before.
2016 Ieee International Conference on Big Data (big Data) Datalography: Scaling Datalog Graph Analytics on Graph Processing Systems

—This paper presents the first Datalog evaluation engine for executing graph analytics over BSP-style graph processing engines. Building on recent advances in Datalog that support efficient evaluation of aggregates functions, it is now easy for data scientists to author many important graph algorithms succinctly. Without the burden of low-level parallelization and optimization, data scientists can avoid programming to the quirks of the latest high-performance distributed computing framework. Where prior approaches build bespoke evaluation engines or modify generalized dataflow processing engines to achieve performance, this work shows how to efficiently evaluate Datalog directly on BSP-style graph processing engines such as Giraph. Datalography incorporates both traditional Datalog optimizations, such as semi-naive evaluation, and new evaluation algorithms and optimization techniques for efficient distributed evaluation of Datalog queries on graph processing engines. In particular we develop evaluation techniques that take advantage of super vertices, eager aggregation, and asynchronous execution to optimize graph processing on Pregel-like systems. We implement our algorithms on top of Apache Giraph and our results indicate that Datalography competes with native, tuned implementations, with some analytics running up to 9 times faster.
ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information

As the technique that determines the position of a target device based on wireless measurements, Wi-Fi localization is attracting increasing attention due to its numerous applications and the widespread deployment of Wi-Fi infrastructure. In this paper, we propose ConFi, the first convolutional neural network (CNN)-based Wi-Fi localization algorithm. Channel state information (CSI), which contains more position related information than traditional received signal strength, is organized into a time-frequency matrix that resembles image and utilized as the feature for localization. The ConFi models localization as a classification problem and addresses it with a five layer CNN that consists of three convolutional layers and two fully connected layers. The ConFi has a training stage and a localization stage. In the training stage, the CSI is collected at a number of reference points (RPs) and used to train the CNN via stochastic gradient descent algorithm. In the localization stage, the CSI of the target device is fed to the CNN and the localization result is calculated as the weighted centroid of the RPs with high output value. Extensive experiments are conducted to select appropriate parameters for the CNN and demonstrate the superior performance of the ConFi over existing methods.