Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Constraints in the Development of Receptive Field Structure of Simple Cells using BCM Learning

Experimentally studies have shown that visual cortical neurons apply BCM (Binenstock Cooper and Munro) learning rule for modifications in synaptic strength. BCM rule uses adaptive threshold and in this both long term potentiation (LTP) and long term depression (LTD) is automatically taken care of. This overcomes the major disadvantage of Hebbian learning in which there is a mechanism only for LTP and no mechanism for LTD. Based on the above-mentioned experimental findings we apply BCM learning rule for the development of orientation selectivity by simple cells. We find that BCM learning rule is sufficient for segregation of ON and OFF regions in developed receptive field (RF) structure of simple cells. Starting from unsegregated ON OFF regions we obtain elongated segregated ON and OFF regions in the RF structure very similar to actual RF structure of simple cells. The orientation selectivity thus developed is also very similar to what is found in actual simple cells.
OPEN MICROPHONE SPEECH UNDERSTANDING: CORRECT DISCRIMINATION OF IN DOMAW SPEECH

An ideal spoken dialogue system listens continually and determines which utterances were spoken to it, understands them and responds appropriately while ignoring the rest This paper outlines a simple method for achieving this goal which involves trading a slight! y higher false rejection rate of in domain utterances for a higher correct rejection rate of Out of Domain (OOD) utterarces. The system recognizes semantic entities specified by a unification grammar which is specialized by Explanation Based Learning (EBL). so that it only uses rules which are seen in the training data. The resulting grammar has probabilities assigned to each consnuct so that overgeneralizations are not a problem. The resulting system only recognizes utterances which reduce to a valid logical form which has meaning for the system and rejects the rest. A class N-gam grammar has been trained on the same training data. This system gives good recognition perfoimance and offers good OUT of dozain discrimination when combined with the senmtic analysis. The resulting systems were tested on a Space Station Robot Dialogue Speech Database and a subset of the OGI conversational speech database. Both systems run in real time on a PC laptop and the present performance allonzs continuous listenisg with an zcceptzb? y ! m fa! se zcceptzce r2te. This type of open microphone system has been used in the Clarissa procedure reading and nalrigation spoken dialogue system which is being tested on the International Space Station.
EVOCROS: results for OAEI 2018

This paper describes EVOCROS, a cross-lingual ontology alignment system suited to create mappings between ontologies described in different natural language. Our tool combines semantic and syntactic similarity measures in a weighted average metric. The semantic is computed via NASARI vectors used together with BabelNet, which is a domain-neutral semantic network. The tool employs automatic translation to a pivot language to consider the similarity. EVOCROS was tested and obtained high quality alignment in the Multifarm dataset. We discuss the experimented configurations and the achieved results in OAEI 2018. This is our first participation in OAEI.
Continuous Authentication Using Biometrics: Data, Models, and Metrics

User authentication is the process of verifying whether the identity of a user is genuine prior to granting him or her access to resources or services in a secured environment. Traditionally, user authentication is performed statically at the point of entry of the system; however, continuous authentication (CA) seeks to address the shortcomings of this method by providing increased session security and combating insider threat.Continuous Authentication Using Biometrics: Data, Models, and Metrics presents chapters on continuous authentication using biometrics that have been contributed by the leading experts in this recent, fast growing research area. These chapters collectively provide a thorough and concise introduction to the field of biometric-based continuous authentication. The book covers the conceptual framework underlying continuous authentication and presents detailed processing models for various types of practical continuous authentication applications.
Biological pathways of exposure and ecotoxicity values for uranium and associated radionuclides: Chapter D in Hydrological, geological, and biological site characterization of breccia pipe uranium deposits in Northern Arizona

287 Introduction.287 Literature Compilation . 288 Sources of Uranium in Northern Arizona . 288 Chemical Speciation of Uranium as It Relates to Biota . 289 Biological Receptors for Exposure Effects Associated with Uranium Mining . 294 Ecotoxicity Values . 305 Chemical Hazards of Uranium . 306 Biological Receptors in the Aquatic Food Chain . 306 Algae, Cyanobacteria, and Aquatic Microorganisms . 306 Aquatic Vascular Plants . 318 Aquatic Invertebrates . 318 Fish . 318 Biological Receptors in the Terrestrial Food Chain . 318 Soil Microorganisms . 318 Terrestrial Nonvascular and Vascular Plants . 318 Terrestrial Invertebrates . 319 Amphibians and Reptiles . 319 Birds . 319 Mammals . 319 Bioconcentration Factors and Trophic Transfers . 320 Aquatic Ecosystem Exposure . 320 Terrestrial Ecosystem Exposure . 321 Chemical Hazards of Radionuclides . 322 Thallium . 322 Algae, Cyanobacteria, and Aquatic Microorganisms . 323 Aquatic Vascular Plants . 323 Aquatic Invertebrates . 323 Fish . 323 Terrestrial Vascular Plants . 323 Amphibians and Reptiles . 323 Mammals . 323 Thorium . 324 Algae, Cyanobacteria, and Aquatic Microorganisms . 325 Aquatic Invertebrates and Fish . 325 Mammals . 325 Bismuth . 325 Radium . 326 Radon . 326 Polonium . 327 Protactinium.327 Radiation Hazards of Radionuclides . 327 Biological Receptors in the Aquatic Food Chain . 330 Algae, Cyanobacteria, and Aquatic Vascular Plants . 330 Aquatic Invertebrates . 330 Fish . 330 Biological Receptors in the Terrestrial Food Chain . 333 Terrestrial Nonvascular and Vascular Plants . 333 Terrestrial Invertebrates . 333 Amphibians and Reptiles . 334 Birds . 334 Mammals . 334 Radionuclide and Radiation Transfers in Aquatic and Terrestrial Ecosystems . 335 Data Gaps, Uncertainties, and Summary . 335 Data Gaps and Uncertainties . 336 Summary.338 Acknowledgments . 338 References Cited.339 Appendix 1. 235Uranium and 232Thorium Decay Series . 351 Appendix 2. Radiation Measurement . 353
Partitioning Evapotranspiration Over the Continental United States Using Weather Station Data

Accurately characterizing evapotranspiration is critical when predicting the response of the hydrologic cycle to climate change. Although Earth system models estimate similar magnitudes of global evapotranspiration, the magnitude of each contributing source varies considerably between models due to the lack of evapotranspiration partitioning data. Here we develop an observation‐based method to partition evapotranspiration into soil evaporation and transpiration using meteorological data and satellite soil moisture retrievals. We apply the methodology at 1,614 weather stations across the continental United States during the summers of 2015 and 2016. We evaluate the method using vegetation indices inferred from satellites, finding strong spatial correlations between modeled transpiration and solar‐induced fluorescence (r2 = 0.87), and modeled vegetation fraction and leaf area index (r2 = 0.70). Since the sensitivity of evapotranspiration to environmental factors depends on the contribution of each source component, understanding the partitioning of evapotranspiration is increasingly important with climate change.
Generalized Stochastic Gradient Learning

We consider forward-looking models with agents following stochastic gradient (SG) adaptive learning. We examine how the conditions for stability of SG learning both differ from and are related to Estability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. However, generalized SG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity.
Authentication in an Internet Banking Environment

On August 8, 2001, the FFIEC agencies (agencies) issued guidance entitled Authentication in an Electronic Banking Environment (2001 Guidance). The 2001 Guidance focused on risk management controls necessary to authenticate the identity of retail and commercial customers accessing Internet-based financial services. Since 2001, there have been significant legal and technological changes with respect to the protection of customer information; increasing incidents of fraud, including identity theft; and the introduction of improved authentication technologies. This updated guidance replaces the 2001 Guidance and specifically addresses why financial institutions regulated by the agencies should conduct risk-based assessments, evaluate customer awareness programs, and develop security measures to reliably authenticate customers remotely accessing their Internet-based financial services.
Digital watermarking of low bit-rate advanced simple profile MPEG-4 compressed video

A novel MPEG-4 compressed domain video watermarking method is proposed and its performance is studied at video bit rates ranging from 128 to 768 kb/s. The spatial spread-spectrum watermark is embedded directly into compressed MPEG-4 bitstreams by modifying DCT coefficients. A synchronization template combats geometric attacks, such as cropping, scaling, and rotation. The method also features a gain control algorithm that adjusts the embedding strength of the watermark depending on local image characteristics, increasing watermark robustness or, equivalently, reducing the watermark's impact on visual quality. A drift compensator prevents the accumulation of watermark distortion and reduces watermark self-interference due to temporal prediction in inter-coded frames and AC/DC prediction in intra-coded frames. A bit-rate controller maintains the bit rate of the watermarked video within an acceptable limit. The watermark was evaluated and found to be robust against a variety of attacks, including transcoding, scaling, rotation, and noise reduction.
Adaptive Security in the Threshold Setting : From Cryptosystems to Signatures by

Threshold cryptosystems and signatures schemes provide ways to distribute trust throughout a group and increase the availability of cryptographic systems. A standard approach in designing these protocols is to base them upon existing single-party systems having the desired properties. Two recent signature schemes [13, 18] have been developed which are provably secure using only some standard number-theoretic hardness assumptions. Both schemes rely upon inversion of a prime number modulo a secret value. We provide a multiparty modular inversion protocol that is secure against an adaptive adversary, thereby enabling threshold signature schemes with stronger security properties than any previous result. As a tool, we also develop an adaptively-secure, erasure-free threshold version of the Paillier cryptosystem. Because of its homomorphic properties, this cryptosystem is of independent interest and is useful in the context of secure elections, lotteries, and general multiparty computation. Thesis Supervisor: Ronald L. Rivest Title: Viterbi Professor of Computer Science and Engineering Thesis Supervisor: Anna Lysyanskaya Title: Co-supervisor
e-Publications@Marquette e-Publications@Marquette

The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three speciﬁc research problems under this architecture. First, we introduce a network-slicing based architecture and explain why and where artiﬁcial intelligence (AI) should be incorporated into this architecture. Second, the motivation, research challenges, existing works, and potential future directions related to applying AI-based approaches in three research problems are described in detail, i.e., ﬂexible radio access network slicing, automated radio access technology selection, and mobile edge caching and content delivery. In summary, this paper highlights the beneﬁts and potentials of AI-based approaches in the research of NGWNs
Teddy: a sketching interface for 3D freeform design

We present a sketching interface for quickly and easily designing freeform models such as stuffed animals and other rotund objects. The user draws several 2D freeform strokes interactively on the screen and the system automatically constructs plausible 3D polygonal surfaces. Our system supports several modeling operations, including the operation to construct a 3D polygonal surface from a 2D silhouette drawn by the user: it inflates the region surrounded by the silhouette making wide areas fat, and narrow areas thin. Teddy, our prototype system, is implemented as a JavaTM program, and the mesh construction is done in real-time on a standard PC. Our informal user study showed that a first-time user typically masters the operations within 10 minutes, and can construct interesting 3D models within minutes.
Image Compression using Singular Value Decomposition

The Singular Value Decomposition expresses image data in terms of number of eigen vectors depending upon the dimension of an image. The psycho visual redundancies in an image are used for compression. Thus an image can be compressed without affecting the image quality. This paper presents one such image compression technique called as SVD. Basic mathematics of SVD is dealt with in detail and results of applying SVD on an image are also discussed. The MSE and compression ratio are used as thresholding, parameters for reconstruction. SVD is applied on variety of images for experimentation. The work is concentrated to reduce the number of eigen values required to reconstruct an image.
AIS Electronic Library (AISeL)

The increasing online content has influenced users’ buying behavior. It has triggered a paradigm shift in marketing strategies, as the consumer is no longer swayed by marketers, instead relying on user comments for a particular product or service. This paper focuses on extracting information from feedbacks like suggestions and recommendation by the users that is often present along with the sentiment. While Sentiment Analysis looks at extraction of consumer sentiment, our focus is on extracting actionable feedback present in the text for use by different stakeholders like business analysts and the customer. Our focus is on mining the key suggestions present in text which would benefit the product developer. We present our results and observations in the paper.
Any AND-OR Formula of Size N can be Evaluated in time N1/2+o(1) on a Quantum Computer

For any AND-OR formula of size N, there exists a bounded-error N1/2+o(1)-time quantum algorithm, based on a discrete-time quantum walk, that evaluates this formula on a black-box input. Balanced, or "approximately balanced," formulas can be evaluated in O(radicN) queries, which is optimal. It follows that the (2-o(1))th power of the quantum query complexity is a lower bound on the formula size, almost solving in the positive an open problem posed by Laplante, Lee and Szegedy.
International Journal of Public Health Science (IJPHS)

Received Mar 26, 2022 Revised Aug 19, 2022 Accepted Sep 7, 2022 Pre-pandemic studies had established that human resilience is mainly based on the social feedback that enables the formation of one’s perceived social support (PSS). In the Malaysian context, the enforcement of the movement control order (MCO) amidst the pandemic altered the social interaction pattern. The shift had involved more dependence on online communication (i.e., social media). Therefore, the way PSS plays its role in predicting resilience could have been affected. Due to the reason mentioned above and the reports that Malaysians tend to increase their levels of spirituality during the MCO, we hypothesized that the sense of being empowered fully mediates the contribution of PSS on resilience while moderated by the levels of spirituality. Four hundred and five adults who reside in Malaysia during the MCO 2 were recruited to respond to scales of PSS, spirituality, empowerment, and resilience through the online survey. Our results suggested that the sense of empowerment significantly and fully mediated the relationship between PSS and resilience among individuals with low and moderate levels of spirituality. Scope, limitations, implications, and suggestions were also discussed at the end of this paper.
Complete natural gradients for structured variational approximations in mixtures of exponential families

Stochastic gradient methods has enabled variational inference for high-dimensional models and large data sets. However, the direction of steepest ascent in the parameter space of a statistical model is not given by the commonly used Euclidean gradient, but the natural gradient which premultiplies the Euclidean gradient by the inverse of the Fisher information matrix. Use of natural gradients in optimization can improve convergence significantly, but inverting the Fisher information matrix is daunting in high-dimensions. Here we consider structured variational approximations with a minimal conditional exponential family representation, which include highly flexible mixtures of exponential family distributions that can fit skewed or multimodal posteriors. We derive complete natural gradient updates for this class of models, which albeit more complex than the natural gradient updates presented prior to this article, account fully for the dependence between the mixing distribution and the distributions of the components. Further experiments will be carried out to evaluate the performance of the complete natural gradient updates.
Appendix for MetaFun: Meta-Learning with Iterative Functional Updates

Functional gradient descent (Mason et al., 1999; Y. Guo & Williamson, 2001) is an iterative optimisation algorithm for finding the minimum of a function. However, the function to be minimised is now a function on functions (functional). Formally, a functional L : H → R is a mapping from a function space H to a 1D Euclidean space R. Just like gradient descent in parameter space which takes steps proportional to the negative of the gradient, functional gradient descent updates f following the gradient in function space. In this work, we only consider a special function space called Reproducing kernel Hilbert space (RKHS) (Appendix A.1), and calculate functional gradients in RKHS (Appendix A.2). The algorithm is further detailed in Appendix A.3.
Open Design Spaces – Socially Crafting Interactive Experiences

Engaging end-users and user communities to take an active part in the cocreation, evolution, and appropriation of modern, interactive systems has become an increasingly important issue over the last years. Bringing together existing research and experiences as well as new challenges such as long-term, large-scale, or highly distributed stakeholders has led to the notion of Open Design Spaces (ODS) to frame and reflect current developments of distributed co-design. Several, formerly often separated strands of research covering different aspects of these challenges have emerged and led to a growing community of researchers and practitioners building on concepts such as Participatory Design, Meta-Design, and End-User Development. The 2nd International Workshop on Open Design Spaces (ODS 2010) focused particularly on social aspects and community co-creation in Open Design Spaces. 5 Proceedings of the 2nd International Workshop on Open Design Spaces (ODS 2010) International Reports on Socio-Informatics, vol. 7, no. 2 (2010), pp. 5-7
Pose Classification and Walking Speed Estimation using Handheld Devices

We describe and evaluate two methods for pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 1215% (straight/indoor walk) of ground truth compared to 1722% for previous work. Implementation on a mobile phone demonstrates that both methods can run online. Author
The RMITCSIRO Ad Hoc, QA, Web, Interactive, and Speech Experiments at TREC 8

The constants k1, k3 and b were set to 1.2, 1000 and 0.75 respectively, as recommended by the City University group [13]. Wd is the length of the document d in bytes and avr Wd is the average document length in the entire collection. N is the total number of documents in the collection, ft is the number of documents in which term t occurs, and fx;t is the frequency of term t in either a document d, passage p or query q. For passage ranking, queries and passages were matched using a non-normalised version of the cosine similarity function:
The Open Neuroimaging Journal

: Background: Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival.
Dataset Evaluation User Generation Renderer Image Population a ) Dataset Parameter Volume Parameter Generation Volume Renderer Image

This paper presents a novel approach to assist the user in exploring appropriate transfer functions for the visualization of volumetric datasets. The search for a transfer function is treated as a parameter optimization problem and addressed with stochastic search techniques. Starting from an initial population of (random or pre-de ned) transfer functions, the evolution of the stochastic algorithms is controlled by either direct user selection of intermediate images or automatic tness evaluation using user-speci ed objective functions. This approach essentially shields the user from the complex and tedious \trial and error" approach, and demonstrates e ective and convenient generation of transfer functions.