Miracle-dz/UniFAR-FTUs huggingface dataset:
28.1 Probabilistically Checkable Proofs

In this lecture we continue our discussion of inapproximability by discussing results based on prob-abilistically checkable proofs (PCPs). In particular, we show how the PCP Theorem, a deep result in computational complexity, is essentially equivalent to the inapproximability of MAX-3SAT. We also show how a strengthening of the PCP Theorem leads to a tight inapproximability result for MAX-3SAT, and we discuss other issues related to PCPs. Recall the template developed in the last lecture for proving inapproximability results. One exhibits a reduction from an N P-complete problem such as SAT to an optimization (say, maximization) problem L in such a way that Yes instances are mapped to instances with optimal objective value at least some α and No instances are mapped to instances with optimal objective value at most some β. The existence of such a reduction proves that L is N P-hard to approximate within any factor better than α/β. In the last lecture we saw examples where standard N P-hardness reductions were used to obtain weak inapproximability results in the above way, and self-reductions were used to magnify the hardness factor achieved. These reductions primarily exploited properties of the problem for which an inapproximability result was desired, i.e. the problem being reduced to. In this lecture, we instead focus on properties of the problem being reduced from. The problem being reduced from is an N P-complete problem, or more generally an arbitrary problem in N P. We now revisit the definition of N P and see how different characterizations of N P can afford additional structure that we can exploit in our gap-introducing reductions. Ideally, we would like the Yes instances to look very different from the No instances in some sense, so that it's easier to introduce a gap in the reduction. Definition 28.1. 1 A language L is in N P if there exists a polynomial-time verifier V and a constant c such that for an input x of length n, the following two properties are satisfied. 1) (Completeness) If x ∈ L then there exists a witness y of length O(n c) such that V accepts the pair x, y. 2) (Soundness) If x ∈ L then for all witnesses y of length O(n c), V rejects x, y. The terminology completenss and soundness is a carry-over from logic, where completenss refers to the property that all true statements are provable in a given proof system, …
An Automated Breast Cancer Recognition Application

Breast cancer is one serious disease that is causing a high rate of mortality worldwide. Thus, it is critical to devise applications that can help in the early detection and diagnosis of this type of cancer as it spreads rapidly to the rest of the body. At present, mammography is the technique used to detect abnormalities in masses and calcifications in the breast. However, in the early stage, masses are very small and granular which makes early detection is challenging task. With the advances in the field of computer vision, image processing techniques can be applied on medical images to develop automated breast recognition system for early detection. This paper proposes an approach that uses texture description of images known as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) to represent features in the breast. After the extraction of masses or calcification, LBP and HOG were applied on the image. Both methods showed a very high recognition rate compared to other existing breast cancer detection system
An architecture for probabilistic concept-based information retrieval

While concept-based methods for information retrieval can provide improved performance over more conventional techniques, they require large amounts of effort to acquire the concepts and their qualitative and quantitative relationships. This paper discusses an architecture for probabilistic concept-based information retrieval which addresses the knowledge acquisition problem. The architecture makes use of the probabilistic networks technology for representing and reasoning about concepts and includes a knowledge acquisition component which partially automates the construction of concept knowledge bases from data. We describe two experiments that apply the architecture to the task of retrieving documents about terrorism from a set of documents from the Reuters news service. The experiments provide positive evidence that the architecture design is feasible and that there are advantages to concept-based methods.
Load Scheduling in a Cloud Based Massive Video-Storage Environment

We propose an architecture for a storage system of surveillance videos. Such systems have to handle massive amounts of incoming video streams and relatively few requests for replay. In such a system load (i.e., Write requests) scheduling is essential to guarantee performance. Large-scale data-storage system (LSDSS) is an emerging hosting facility for video-storage, which has a very high number of writes while most of the videos are never or rarely watched. We discuss the design and implementation of LSDSS and load scheduling in autonomous storage environments called datacenters in LSDSS. A datacenter (DC) is the basic concept in our LSDSS, which has the self-management system to store data efficiently. A LSDSS consists of many DCs organized in a hierarchy fashion, thereby decentralizing load scheduling tasks. Because DC has a simple design, load scheduling is particularly suited for implementation on a real-time video surveillance and allows to make scheduling decisions. We also discuss experimental results that clearly show the advantage of load scheduling over the widely known base load scheduling.
PyNET-Q × Q: A Distilled PyNET for Q × Q Bayer Pattern Demosaicing in CMOS Image Sensor

The deep learning-based ISP models for mobile cameras produce high-quality images comparable to the professional DSLR camera. However, many of them are computationally expensive, which may not be appropriate for mobile environments. Also, the recent mobile cameras adopt non-Bayer CFAs (e.g., Quad Bayer, Nona Bayer, and Q × Q Bayer) to improve image quality; however, most deep learning-based ISP models mainly focus on standard Bayer CFA. In this work, we propose PyNET-Q × Q based on PyNET, a light-weighted ISP explicitly designed for the Q × Q CFA pattern. The number of parameters of PyNET-Q × Q is less than 2.5% of PyNET. We also introduce a novel knowledge distillation technique, progressive distillation, to train the compressed network effectively. Finally, experiments with Q × Q images (obtained by an actual Q × Q camera sensor, under development) demonstrate the outstanding performance of PyNET-Q × Q despite significant parameter reductions.
Rethinking the digital divide

In discussing and planning interventions around the “digital divide” people tend to think in terms of the binary oppositions of “digital-haves” and “digital-have-nots”. Information and communications technologies (ICT) programs sponsored by governments and other agencies to address the “digital divide” also tend to be “topdown” initiatives that focus on the provision of institutional aid and the development of infrastructure. Within these approaches ICT have-nots tend to be treated as the passive receivers of aid. The agency of digital-havenots in gaining access to ICT has been rarely examined. In this paper we report on a study that has shown that ICT use patterns amongst those with poor access and utilization of ICT is diverse and complex. Results from a survey of 495 rural-urban migrants in Beijing show that self-initiated digital transition exists among this group of people. The transition from digital-have-not to digital-have is not a one-step process but rather an incremental process with multiple stages and multiple pathways. Findings from this study will provide helpful insights for policy makers and related stakeholders when discussing and planning ICT programs and activities designed to address the issues associated with the digital divide.
A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

Using echo-top height and hourly rainfall datasets, a new reflectivity-rainfall (Z-R) relationship is established in the present study for the radar-based quantitative precipitation estimation (RQPE), taking into account both the temporal evolution (dynamical) and the types of echoes (i.e., based on echo-top height classification). The new Z-R relationship is then applied to derive the RQPE over the middle and lower reaches of Yangtze River for two short-time intense rainfall cases in summer (2200 UTC 1 June 2016 and 2200 UTC 18 June 2016) and one stratiform rainfall case in winter (0000 UTC 15 December 2017), and then the comparative analyses between the RQPE and the RQPEs derived by the other two methods (the fixed Z-R relationship and the dynamical Z-R relationship based on radar reflectivity classification) are accomplished. The results show that the RQPE from the new Z-R relationship is much closer to the observation than those from the other two methods because the new method simultaneously considers the echo intensity (reflecting the size and concentration of hydrometers to a certain extent) and the echo-top height (reflecting the updraft to a certain extent). Two statistics of 720 rainfall events in summer (April to June 2017) and 50 rainfall events in winter (December 2017) over the same region show that the correlation coefficient (root-mean-squared error and relative error) between RQPE derived by the new Z-R relationship and observation is significantly increased (decreased) compared to the other two Z-R relationships. Besides, the new Z-R relationship is also good at estimating rainfall with different intensities as compared to the other two methods, especially for the intense rainfall.
PARTICLES 2017: Discrete Modelling in Cell & Tissue Mechanobiology: Invited Session 9

Learning is mediated by activity-dependent changes in synaptic strength that rely on Calcium-dependent signalling in the postsynaptic neuron. In the hippocampus, synapses are located on dendritic spines, small mushroom-like structures that grow from the dendrite. Dendritic spines form micro-compartments that are - to some extent - chemically isolated from the rest of the dendrite due to the long and narrow spine neck limiting diffusion in and out. The shape and the physical structure of a dendritic spine are determined by the actin filaments that form the cellular cytoskeleton. Long-term potentiation (LTP) of the synapse leads to remodelling of the polymeric actin cytoskeleton, which increases the size of the dendritic spine. This process is regulated by interactions between actin and other postsynaptic proteins [1]. Here, addressing the lack of spatiotemporal models that combine structure with biochemical signaling, we present a set of methods that allows the modelling of the biochemistry and spatial dynamics of actin filament dynamics using the spatial stochastic simulator MCell [2]. These methods allow us recapitulate the main events relevant for actin filament remodeling, including polymerisation, depolymerisation, branching and severing followed by filament displacement in space. They rely on the ability of MCell to model multi-state complex molecules [3] and on a system of virtual tags to label states of actin subunits. The complex molecule feature was utilised by defining an immobile three-dimensional matrix with 100 3 subunits, each belonging to one of the
Stereo Image Coder Based on theMRFModel for Disparity Compensation

This paper presents a stereoscopic image coder based on the MRF model and MAP estimation of the disparity field. The MRF model minimizes the noise of disparity compensation, because it takes into account the residual energy, smoothness constraints on the disparity field, and the occlusion field. Disparity compensation is formulated as an MAP-MRF problem in the spatial domain, where the MRF field consists of the disparity vector and occlusion fields. The occlusion field is partitioned into three regions by an initial double-threshold setting. The MAP search is conducted in a block-based sense on one or two of the three regions, providing faster execution. The reference and residual images are decomposed by a discrete wavelet transform and the transform coefficients are encoded by employing the morphological representation of wavelet coefficients algorithm. As a result of the morphological encoding, the reference and residual images together with the disparity vector field are transmitted in partitions, lowering total entropy. The experimental evaluation of the proposed scheme on synthetic and real images shows beneficial performance over other stereoscopic coders in the literature.
Development and Training of a Neural Controller for Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot Hind Leg Walking in a Dog Robot

Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal’s body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation
Middleware for Clouds and e‐Science

This special issue focuses on middleware for Clouds and e-Science applications, and is based on extended, thoroughly revised papers from the 8th International Workshop on Middleware for Grid, Clouds and e-Science (MGC 2010) and the Workshop on Challenges in e-Science (CIS 2010). The authors were invited to provide extended versions of their original papers taking into account comments and suggestions raised during the peer review process and comments from the audience during the workshops. Relevant contributions have been provided by Beloglazov and Buyya [1], Veiga et al. [2], Klôh et al. [3], Mathias and Baude [4], de Alencar et al. [5], Porto et al. [6], Ogasawara et al. [7], and Oliveira et al. [8]. These contributions focus on:
Secure Internet Connectivity for Dynamic Source Routing ( DSR ) based Mobile Ad hoc Networks

'Internet connectivity to Mobile Ad hoc networks' have been dealt separately in the past research. This paper proposes a light weight solution for secure routing in integrated Mobile Ad hoc Network (MANET)-Internet. The proposed framework ensures mutual authentication of Mobile Node (MN), Foreign Agent (FA) and Home Agent (HA) to avoid various attacks on global connectivity and employs light weight hop-by-hop authentication and end-to-end integrity to protect the network from most of the potential security attacks. The framework also uses dynamic security monitoring mechanism to monitor the misbehavior of internal nodes. Security and performance analysis show that our proposed framework achieves good security while keeping the overhead and latency minimal.
3-D scene representation as a collection of images

We address the problem of the prediction of new views of a given scene from existing weakly or fully calibrated views called reference views. Our method does not make use of a three-dimensional model of the scene, but of the existing relations between the images. The new views are represented in the reference views by a viewpoint and a retinal plane, i.e. by four points which can be chosen interactively. From this representation and from the constraints between the images, we derive an algorithm to predict the new views. We discuss the advantages of this method compared to the commonly used scheme: 3D reconstruction-projection.
POLITECNICO DI TORINO Repository ISTITUZIONALE

—Security tools have evolved dramatically in the recent years to combat the increasingly complex nature of attacks, but to be effective these tools need to be conﬁgured by experts that understand network protocols thoroughly. In this paper we present FieldHunter, which automatically extracts ﬁelds and infers their types; providing this much needed information to the security experts for keeping pace with the increasing rate of new network applications and their underlying protocols. FieldHunter relies on collecting application messages from multiple sessions and then applying statistical correlations is able to infer the types of the ﬁelds. These statistical correlations can be between different messages or other associations with meta-data such as message length, client or server IPs. Our system is designed to extract and infer ﬁelds from both binary and textual protocols. We evaluated FieldHunter on real network trafﬁc collected in ISP networks from three different continents. FieldHunter was able to extract security relevant ﬁelds and infer their nature for well documented network protocols (such as DNS and MSNP) as well as protocols for which the speciﬁcations are not publicly available (such as SopCast) and from malware such as (Ramnit).
Montclair State University Digital Commons Montclair State University Digital Commons

The complexity and nonlinearities of the modern power grid render traditional physical modeling and mathematical computation unrealistic. AI and predictive machine learning techniques allow for accurate and efficient system modeling and analysis. Electricity consumption forecasting is highly valuable in energy management and sustainability research. Furthermore, accurate energy forecasting can be used to optimize energy allocation. This thesis introduces Deep Learning models including the Convolutional Neural Network (CNN), the Recurrent neural network (RNN), and Long Short-Term memory (LSTM). The Hourly Usage of Energy (HUE) dataset for buildings in British Columbia is used as an example for our investigation, as the dataset contains data from residential customers of BC Hydro, a provincial power utility company. Due to the temporal dependency in time-series observation data, data preprocessing is required before a model can be created. The LSTM model is utilized to create a predictive model for electricity consumption as output. Approximately 63% of the data is used for training, and the remaining 37% is used for testing. Various LSTM parameters are tested and tuned for best performance. Our LSTM predictive model can facili tate power companies’ resource management decisions.
Mining Needles in a Haystack : Classifying Rare Classes via Two-Phase Rule Induction

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ACM SIGMOD 2001 May 21-24, Santa Barbara, California, USA Copyright 2001 ACM 1-58113-332-4/01/05 . 45.00.
Intrusion Detection based on Distance Combination

— The intrusion detection problem has been frequently studied, but intrusion detection methods are often based on a single point of view, which always limits the results. In this paper, we introduce a new intrusion detection model based on the combination of different current methods. First we use a notion of distance to unify the different methods. Second we combine these methods using the Pearson correlation coefficients, which measure the relationship between two methods, and we obtain a combined distance. If the combined distance is greater than a predetermined threshold, an intrusion is detected. We have implemented and tested the combination model with two different public data sets: the data set of masquerade detection collected by Schonlau & al., and the data set of program behaviors from the University of New Mexico. The results of the experiments prove that the combination model has better performances.
Collaborative Information Seeking [Guest editors' introduction]

People often engage in collaborative information seeking to achieve common goals, enlist expertise, increase the scope of what can be searched, and even simply to help others. In today's era of increased user-generated content and online sharing, computer and information scientists have a renewed interest in collaborative information seeking as an exciting area of research and development, with applications that range from education to e-commerce, with implications for areas that range from libraries to legal informatics. The Web extra at http://youtu.be/CXnNut7hKsU is a video in which proponents of collaborative information seeking discuss why supporting people's innate behavior and the need for working together while seeking and using information are important to computer science. Featured speakers include Chirag Shah, Rob Capra, Madhu Reddy, Meredith Ringel Morris, Roberto Gonzalez-Ibanez, and Michael B. Twidale.
Classification for Suicidology Jonathon

Computational techniques for topic classification can support qualitative research by automatically applying labels in preparation for qualitative analyses. This paper presents an evaluation of supervised learning techniques applied to one such use case, namely that of labeling emotions, instructions and information in suicide notes. We train a collection of one-versus-all binary support vector machine classifiers, using cost-sensitive learning to deal with class imbalance. The features investigated range from simple bag-of-words andn-grams over stems, to information drawn from syntactic dependency analysis and WordNet synonym sets. The experimental results are complemented by an analysis of systematic errors in both the output of our system and the gold-standard annotations.
An In-Depth Analysis of the Learning and Study Strategies Inventory (LASSI)

This study explores the latent structure of scores on the Learning and Study Strategies Inventory (LASSI) and analyzes the relationship between this structure and students' academic performance. Two independent samples of college freshmen (n = 527) and seniors (n = 429) completed the LASSI. Data analysis of the first sample revealed acceptable psychometric properties and suggested a three-factor model, which was supported by a confirmatory analysis of the second sample data. The three latent constructs, labeled Affective Strategies, Goal Strategies, and Comprehension Monitoring Strategies, were shown to be interrelated, and the first two were positively linked to academic performance. The usefulness and rationale of the latent structure of the LASSI and the potential use of its scales are discussed.
Prefetch-Aware Shared-Resource Management for Multi-Core Systems Eiman Ebrahimi †

Chip multiprocessor (CMP) systems share a large portion of t he memory subsystem among multiple cores. Recent proposals have addressed high-performance and fair management of the se shared resources; however, none of them take into account prefetch requests. Without prefetching, significant perfo rmance is lost, which is why existing systems prefetch. By no t taking into account prefetch requests, all recent shared-resource man agement proposals often significantly degrade both perform ance and fairness, rather than improve them in the presence of prefet ching. This paper is the first to propose mechanisms that both manage the shared resources of a multi-core chip to obtain highperformance and fairness, and also exploit prefetching. We apply our proposed mechanisms to two resource-based manage ment techniques for memory scheduling and one source-throttlin g-based management technique for the entire shared memory s ystem. We show that our mechanisms improve the performance of a 4-co re system that uses network fair queuing, parallelism-awar e batch scheduling, and fairness via source throttling by 11. 0%, 10.9%, and 11.3% respectively, while also significantly improving fairness.