Miracle-dz/UniFAR-FTUs huggingface dataset:
The Virtual Commons : Understanding Content Provision in Peer-to-Peer File Sharing Networks

Peer-to-peer networks have emerged as a popular alternative to traditional client-server architectures for the distribution of information goods. Recent academic studies have observed high levels of free-riding in various peer-to-peer networks, leading some to predict the imminent collapse of communities hosted on these networks. Our research develops static and dynamic analytic models to analyze the behavior of peer-topeer networks in the presence of free-riding. In contrast to previous predictions we find that freeriding is sustainable in equilibrium and in some cases occurs as part of the socially optimal outcome. We also show that network performance can be improved by grouping peers into communities that provide similar value and by providing external incentives to peers to share. With regard to the latter finding, we show that without external incentives, the level of free-riding in peer-to-peer networks will be higher than the socially optimal level, but that quality of service tied to the contribution of content can be used as a lever to induce users to share and thereby achieve the socially optimal network outcome. Our results are robust to consideration of a dynamic game where content is duplicated in the network over time.
Offline Signature Verification And Identification Using Distance Statistics

This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.
BIOINFORMATICS ORIGINAL PAPER

Motivation: The Basic Local Alignment Search Tool (BLAST) is one of the most widely used bioinformatics tools. The widespread impact of BLAST is reﬂected in over 53000 citations that this software has received in the past two decades, and the use of the word ‘blast’ as a verb referring to biological sequence comparison. Any improvement in the execution speed of BLAST would be of great importance in the practice of bioinformatics, and facilitate coping with ever increasing sizes of biomolecular databases. Results: Using a general-purpose graphics processing unit (GPU), we have developed GPU-BLAST, an accelerated version of the popular NCBI-BLAST. The implementation is based on the source code of NCBI-BLAST, thus maintaining the same input and output interface while producing identical results. In comparison to the sequential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 and 4. Availability: The source code of GPU-BLAST is freely available at http
NeuroIS : The Potential of Cognitive Neuroscience for Information Systems Research

This paper introduces the idea of drawing upon the cognitive neuroscience literature to inform IS research (herein termed “NeuroIS”). Recent advances in cognitive neuroscience are uncovering the neural bases of cognitive, emotional, and social processes, and they offer new insights into the complex interplay between IT and information processing, decision-making, and behavior among people, organizations, and markets.
Pepperdine Digital

While students are increasingly struggling with anxiety and depression, the effects of therapy dogs on student stress has only recently been explored. This study was conducted to investigate whether therapy dogs can improve student affect and to determine if these benefits extend to cognition in the form of enhanced ability to remember information. Forty-four college students were randomly assigned to interact with a therapy dog or not during both learning (session 1) and testing (session 2) in a paired-associates procedure. Arousal, stress, and mood were measured at the beginning and end of each session. As predicted, therapy dogs increased happiness and decreased stress and arousal. However, there was no difference in recognition memory for the paired associates between the therapy dog and control conditions. Mood was a significant predictor of memory, such that decreased happiness in session 2 predicted better recognition performance. These findings indicate that the benefits of therapy dogs are primarily affective and not cognitive
Identifying tumor cells at the single cell level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. The single cell sequencing technology enables an effective molecular characterization of single cells within the tumor. This technology can help deconvolute heterogeneous tumor samples and thus revolutionize personalized medicine. However, a governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. One of the critical cell type annotation challenges is identification of tumor cells within single cell or spatial sequencing experiments.This is a critical limiting step for a multitude of research, clinical, and commercial applications. A reliable method addressing that challenge is a prerequisite for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. Here, we propose Ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single cell level. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.
A TGV-Based Framework for Variational Image Decompression, Zooming, and Reconstruction. Part I: Analytics

The present work is the second of two papers on a variational model for image reconstruction whose specific features are twofold: First, data fidelity is realized by interval constraints on the coefficients of a Riesz basis representation, and second, total generalized variation (TGV) of arbitrary order is employed as image prior. While Part I provides a comprehensive analysis of the model in a general function space setting as well as discusses applications such as artifact-free JPEG, JPEG 2000 decompression, and variational zooming, Part II deals with the discretized setting and presents globally convergent algorithms for these applications. In the case of JPEG and JPEG 2000 decompression, highly improved color image reconstructions are obtained from the encoded files. The numerical results are supplemented by duality based stopping criteria, a parallelized implementation, and a comparison of results obtained with TV and second and third order TGV regularization.
Consideration of Landsat-8 Spectral Band Combination in Typical Mediterranean Forest Classification in Halkidiki, Greece

Abstract Utilization of satellite data by means of remote sensing practices creates a wider window of opportunities to conduct robust outcomes Landsat OLI-8 data acquired over the peninsula of Halkidiki in Greece for the summer period of 2013, were analyzed to determine their utility to classify natural resources categories based on the determination of spectral bands combination. Consequently, spectral bands combination can be used to classify various categories based on their higher overall accuracy assessment. Spectral information contained in each of the utilized channels of Landsat OLI-8, statistics defining 12 cover type classes of interest were calculated and used as a basis for classification of the designated study area. Spectral bands combinations (4-5-6/ 3-4-5/ 3-4-5-6/ 4-5-6-7) fulfill the required accuracy for forest purpose as they conducted overall accuracies ranging between 7.85% and 96.29%. The spectral band combination 3-4-5-6-7 conducted the highest overall accuracy (98.15%). The poorest results were obtained by the analysis of spectral bands combination 4-6 (68.52%). Single spectral band 6 conducted the best overall results; however, spectral bands 5 and 6 seemed to be the most useful spectral bands combination for each category.
Ieee Information Theory Society Newsletter President's Column from the Editor Ieee Information Theory Society Newsletter the Historian's Column

Shannon information theory aims at finding fundamental limits for storage and communication, including rates of convergence to these limits. Indeed, many interesting information theoretic phenomena seem to appear in the second order asymptotics. So we first discuss precise analysis of the minimax redundancy that can be viewed as a measure of learnable or useful information. Then we highlight Markov types unveiling some interesting connections to combinatorics of graphical enumeration and linear Diophantine equations. Next we turn our attention to structural compression of graphical objects, proposing a compression algorithm achieving the lower bound represented by the structural entropy. These results are obtained using tools of analytic combinatorics and analysis of algorithms, known also as analytic information theory. Finally, we argue that perhaps information theory needs to be broadened if it is to meet today’s challenges beyond its original goals (of traditional communication) in biology, economics, modern communication, and knowledge extraction. One of the essential components of this perspective is to continue building foundations in better understanding of temporal, spatial, structural and semantic information in dynamic networks with limited resources. Recently, the National Science Foundation has established the first Science and Technology Center on Science of Information (CSoI) to address these challenges and develop tools to move beyond our current understanding of information flow in communication and storage systems.
The Moral Choice Machine : Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices

Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? Here, we show that applying machine learning to human texts can extract deontological ethical reasoning about ”right” and ”wrong” conduct. We create a template list of prompts and responses, which include questions, such as “Should I kill people? ”, “Should I murder people? ”, etc. with answer templates of “Yes/no, I should (not). ” The model’s bias score is now the difference between the model’s score of the positive response (“Yes, I should”) and that of the negative response (“No, I should not”). For a given choice overall, the model’s bias score is the sum of the bias scores for all question/answer templates with that choice. We ran different choices through this analysis using a Universal Sentence Encoder. Our results indicate that text corpora contain recoverable and accurate imprints of our social, ethical and even moral choices. Our method holds promise for extracting, quantifying and comparing sources of moral choices in culture, including technology.
Geo-Replication in Large-Scale Cloud Computing Applications

Cloud computing applications have acquired a signiﬁcant importance in the last years. Therefore, this report addresses the problems of supporting consistency and fault-tolerance in large-scale Geo-replicated systems. We are particularly interested in applications built using the cloud-computing paradigm, where data is maintained in multiple datacenters, located in geographically distant regions. Examples of such applications are Facebook, Google Apps, among others. In such systems, data is replicated in diﬀerent datacenters but also internally to each datacenter, in diﬀerent nodes. The report discusses the diﬀerent trade-oﬀs among consistency, reliability, availability, and performance when implementing these systems and pinpoints some possible directions to improve the state of the art in this respect.
A novel information theoretic approach for finding semantic similarity in WordNet

Information Content (IC) based measures for finding semantic similarity is gaining preferences day by day as semantics of concepts can be highly characterized by information theory. This IC of concept can precisely quantify its generality and concreteness and generates dimensions for better understanding of concept semantics. The conventional way for calculating IC is based on the probability of appearance of concepts in corpora. Due to data sparseness and corpora dependency issues of those conventional approaches, a new corpora independent intrinsic IC calculation measure has evolved and gaining better performance over those conventional measures. In this paper we analyze several intrinsic IC models, emphasize related issues and present a novel information theoretic intrinsic model which can calculate IC of concepts based solely on underlying ontology. Our intense focus stays on several topological structures of the underlying ontology. Accuracy of intrinsic IC calculation measure relies on those factors deeply. Our approach is evaluated and compared with corpora and intrinsic IC based methods based on benchmark data set. Experimental results show that our intrinsic IC model achieves significant results than the existing techniques.
Capital Controls: Myth and Reality - a Portfolio Balance Approach

This paper is a meta-analysis of the literature on capital controls that aims to solve (at least) four very serious apples-to-oranges problems: (i) There is no unified theoretical framework to analyze the macroeconomic consequences of controls; (ii) there is significant heterogeneity across countries and time in the control measures implemented; (iii) there are multiple definitions of what constitutes a "success"; and (iv) the empirical studies lack a common methodology-furthermore these are significantly over-weighted by a couple of country cases (Chile and Malaysia). In this paper, we attempt to address some of these shortcomings by being very explicit about what measures are construed as capital controls. Also, given that success is measured so differently across studies, we sought to standardize the results of the close to 40 empirical studies we summarize in this paper. The standardization was done by constructing two indices of capital controls: Capital Controls Effectiveness Index (CCE Index), and Weighted Capital Controls Effectiveness Index (WCCE Index). The difference between them lies in that the WCCE controls for the differentiated degree of methodological rigor applied in each of the considered papers. Inasmuch as possible, we bring to bear the experiences of less well-known episodes than those of Chile and Malaysia, and the more recent controls on outflows in emerging Europe. We find that only under countryspecific characteristics are capital controls effective, implying that, more often than not, in practice they do not work. We also show that the equivalence in effects of price vs. quantity capital controls is conditional on the level of short-term capital flows.
Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor based human behavior. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.
Trace element content in soils of the King George and Elephant islands, maritime Antarctica

ABSTRACT Trace element concentrations were studied in soils of the King George and Elephant islands in the maritime part of West Antarctica. The lowest concentrations of Cu, Pb, Cd, Zn, Ni and Mn were typical for the pristine soil of Elephant Island. The highest concentrations of these elements were found in the Fildes Peninsula and revealed the influence of human activities in the area of the Bellingshausen station and adjacent waste disposal sites. Ornithogenic soils of the Fildes Peninsula have shown low concentrations of Cd and As. Using geoaccumulation indexes, all the pristine soils of King George and Elephant islands and ornithogenic soils of the Fildes Peninsula were classified as unpolluted; the human-affected soils were mainly identified as moderately polluted. Obtained data can be used as background concentration levels for further researches. GRAPHICAL ABSTRACT
Localizing Q&A Semantic Parsers for Any Language In a Day

We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology allows for automatic generation of training data in the target language by augmenting machine translated datasets with local property values scraped from public websites, trains a semantic parser for the target languages, then validates and tests the model on natural utterances curated using human translators. We assess the effectiveness of our approach by extending the current capabilities of a recently-proposed system for Question Answering (QA) on the open web to 10 foreign languages for the restaurants and hotels domains. We show that best performance can be achieved using a few shot approach where a small proportion of the train set consists of natural human translations of utterances from the English development set. Our model achieves an overall test accuracy ranging between 64.41% and 79.12% for the hotels domain and between 75.6% and 82.4% for restaurants domain, which compares favorably to the English parser trained on gold English data. Our methodology enables any software developer to add a new language capability to any QA system for a new domain in less than 24 hours.1
DIMSUMnet: new directions in wireless networking using coordinated dynamic spectrum

The new paradigm of dynamic spectrum access (DSA) networks aims to provide opportunistic access to large parts of the underutilized spectrum. The majority of research in this area has focused on free-for-all, uncoordinated access methods common in ad-hoc military applications (Horne, W. 2003; Leaves, P. et al., 2002; Lehr, W. et al., 2002; Schafer, D.J.; To/spl uml/njes, R., 2002). We argue that a simpler pragmatic approach that offers coordinated, spatially aggregated spectrum access via a regional spectrum broker is more attractive in the immediate future. We first introduce two new concepts, coordinated access band (CAB) and statistically multiplexed access (SMA), to the spectrum. We describe their implementation in the new DIMSUMnet (dynamic intelligent management of spectrum for ubiquitous mobile-access network) architecture consisting of four elements: base stations; clients; a radio access network manager (RAN-MAN) that obtains spectrum leases; a per-domain spectrum broker that controls spectrum access. We also discuss in detail various issues in the design of spectrum brokers and spectrum allocation policies and algorithms.
Number-theoretic Constructions of Eecient Pseudo-random Functions Preliminary Version

We describe eecient constructions for various cryptographic primitives (both in private-key and in public-key cryptography). We show these constructions to be at least as secure as the decisional version of the Diie-Hellman assumption or as the assumption that factoring is hard. Our major result is a new construction of pseudo-random functions such that computing their value at any given point involves two multiple products. This is much more eecient than previous proposals. Furthermore, these functions have the advantage of being in TC 0 (the class of functions computable by constant depth circuits consisting of a polynomial number of threshold gates) which has several interesting applications. The simple algebraic structure of the functions implies additional features. In particular, we show a zero-knowledge proof for statements of the form \y = f s (x)" and \y 6 = f s (x)" given a commitment to a key s of a pseudo-random function f s .
A Retrospective Analysis of Water Quality in an Open Bay on the Mexican Pacific

ABSTRACT de la Lanza-Espino, G. ; Silva, R. ; Hernández-Pulido, S. ; Mariño-Tapia, I. , and Enríquez, C. , 2017. A retrospective analysis of water quality in an open bay on the Mexican Pacific. In: Martinez, M.L. ; Taramelli, A. , and Silva, R. (eds.), Coastal Resilience: Exploring the Many Challenges from Different Viewpoints. Journal of Coastal Research, Special Issue, No. 77, pp. 39–50. Coconut Creek (Florida), ISSN 0749-0208. Lack of knowledge about the complex interactions between the dynamics of natural systems (physical, chemical and biological) and the need for the resources found in coastal environments has led to the unplanned and disorganized development of several anthropogenic activities in the study area. It is important to revert this tendency and promote medium term studies that can help to foresee the unwanted effects, sometimes irreversible, of human activities on the environment. This paper includes results from hydrodynamic and water quality studies carried out in Petacalco Bay, Guerrero (Mexican Pacific) in order to present elements for future appropriate management of industrial outflows in the region. Around this bay there are important rivers and small villages, a major port, chemical and thermoelectric plants, all discharging their (generally untreated) effluents directly into the sea. The results of this study show that the overall water quality is similar to natural concentrations found in the ocean. Nevertheless, eutrophization was found, associated with the fertilizer industry, and the hot water discharge from the thermoelectric plant generates hot plumes, which dissipate in the environment, despite the intense and variable hydrodynamic characteristics of the region.