Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Privacy-Enhanced Matchmaking Protocol

Although several wide-spread internet applications (e.g., job-referral services, dating services) can benefit from online matchmaking, protocols defined over the past two decades fail to address important privacy concerns. In this paper, we enhance traditional privacy requirements (e.g., user anonymity, matching-wish authenticity) with new privacy goals (e.g., resistance to off-line dictionary attacks, and forward privacy of users’ identities and matching wishes), and argue that privacy-enhanced matchmaking cannot be provided by solutions to seemingly related problems such as secret handshakes, set intersection, and trust negotiation. We define an adversary model, which captures the key security properties of privacy-enhanced matchmaking, and show that a simple, practical protocol derived by a two-step transformation of a password-based authenticated key exchange counters adversary attacks in a provable manner (in the standard model of cryptographic security).
Evaluation of Algorithm Performance in ChIP-Seq Peak Detection

Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of ChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods), each with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting an appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven different peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy and usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers in choosing a suitable tool for handling ChIP-seq data.
Recovering the Optimal Solution by Dual Random Projection

Random projection has been widely used in data classification. It maps high-dimensional data into a low-dimensional subspace in order to reduce the computational cost in solving the related optimization problem. While previous studies are focused on analyzing the classification performance of using random projection, in this work, we consider the recovery problem, i.e., how to accurately recover the optimal solution to the original optimization problem in the high-dimensional space based on the solution learned from the subspace spanned by random projections. We present a simple algorithm, termed Dual Random Projection, that uses the dual solution of the low-dimensional optimization problem to recover the optimal solution to the original problem. Our theoretical analysis shows that with a high probability, the proposed algorithm is able to accurately recover the optimal solution to the original problem, provided that the data matrix is of low rank or can be well approximated by a low rank matrix.
Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.
Homomorphic encryption in mobile multi cloud computing

Multi cloud computing has become a new trend for complementing existing cloud computing today. Multi cloud computing is considered safer and more efficient in maintaining data regulation of user(s). The paper discusses the security of mobile multi cloud computing (MMC) and the advantages for mobile user(s), beside that for the data security itself cover with homomorphic encryption which predictable by many researchers as the optimum method for cloud computing environment. The implementation and evaluation of homomorphic encryption in mobile cloud computing are discussed in this paper.
A performance-optimized model of neural responses across the ventral visual stream

Human visual object recognition is subserved by a multitude of cortical areas. To make sense of this system, one line of research focused on response properties of primary visual cortex neurons and developed theoretical models of a set of canonical computations such as convolution, thresholding, exponentiating and normalization that could be hierarchically repeated to give rise to more complex representations. Another line or research focused on response properties of high-level visual cortex and linked these to semantic categories useful for object recognition. Here, we hypothesized that the panoply of visual representations in the human ventral stream may be understood as emergent properties of a system constrained both by simple canonical computations and by top-level, object recognition functionality in a single unified framework (Yamins et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Güçlü and van Gerven, 2015). We built a deep convolutional neural network model optimized for object recognition and compared representations at various model levels using representational similarity analysis to human functional imaging responses elicited from viewing hundreds of image stimuli. Neural network layers developed representations that corresponded in a hierarchical consistent fashion to visual areas from V1 to LOC. This correspondence increased with optimization of the model’s recognition performance. These findings support a unified view of the ventral stream in which representations from the earliest to the latest stages can be understood as being built from basic computations inspired by modeling of early visual cortex shaped by optimization for high-level object-based performance constraints. Significance Statement Prior work has taken two complimentary approaches to understanding the cortical processes underlying our ability to visually recognize objects. One approach identified canonical computations from primary visual cortex that could be hierarchically repeated and give rise to complex representations. Another approach linked later visual area responses to semantic categories useful for object recognition. Here we combined both approaches by optimizing a deep convolution neural network based on canonical computations to preform object recognition. We found that this network developed hierarchically similar response properties to those of visual areas we measured using functional imaging. Thus, we show that object-based performance optimization results in predictive models that not only share similarity with late visual areas, but also intermediate and early visual areas.
Adaptive Modulation and Coding With Incremental Redundancy Hybrid ARQ in MIMO Systems: A Cross Layered Design.

The present generation wireless systems has significant growth in the demand for reliable high-speed wireless communication links for multimedia applications like voice, video, e-mail, web browsing, etc. Attaining highly reliable links is a challenging task in wireless environment. MIMO exploits space dimension to improve the system capacity, range, and reliability. In this paper a cross layered approach with Adaptive Modulation and Coding (AMC) in the physical layer and Incremental Redundancy Hybrid Automatic Retransmission Request (IR-HARQ) in the data link layer is proposed for the fourth generation MIMO systems. The proposed model improves the transmission rate and also reliability of the MIMO system.
A scalable hardware render accelerator using a modified scanline algorithm

A hardware accelerator for 3D ren&ring, based on a mcdifkd scanline algorithm, is presented. The accelerator renders multiple scanlines in parallel with high efficiency, and is optimized for integration into systems that suppr[ high speed data streams (such as video). The architecture has a very high performance/cost ratio, but maintains a low entry cost and a high degree of scalability — key issues for incorporation in personal computers. The performance of both the general algorithm and the prototype implementation is analyzed.
It's Not Just for Inflation: The Usefulness of the Median CPI in BVAR Forecasting

In this paper we investigate the forecasting performance of the median CPI in a variety of Bayesian VARs (BVARs) that are often used for monetary policy. Until now, the use of trimmed-mean price statistics in forecasting inflation has often been relegated to simple univariate or “Philips-Curve” approaches, thus limiting their usefulness in applications that require consistent forecasts of multiple macro variables. We find that inclusion of an extreme trimmed-mean measure—the median CPI—significantly improves the forecasts of both headline and core CPI. across our wide-ranging set of BVARs. While the inflation forecasting improvements are perhaps not surprising given the current literature on core inflation statistics, we also find that inclusion of the median CPI improves the forecasting accuracy of the central bank’s primary instrument for monetary policy—the federal funds rate. We conclude with a few illustrative exercises that highlight the usefulness of using the median CPI.
Hands-on Small Group versus Whole Class Use of an Interactive Simulation: Qualitative Comparisons

Assumptions about the superiority of hands-on use of computer simulations over projecting them in whole class have seldom been tested. Contrary to expectations, preliminary pre-post results from two lesson sequences yielded no evidence for an advantage for students in the hands-on condition. We conduct qualitative analyses of one of the lesson sequences, in which a popular simulation was used in eight high school physics class sections, half in whole class discussion and half in small groups. Videotape and activity sheet analyses of such factors as percentage of time spent on conceptual difficulties and amount of support for using key visual features of the simulation yielded no evidence for an advantage for small group students. No small group students in lower level physics sections showed evidence in written or drawn work for having utilized key visual features. A balance of complementary small group and whole class use is recommended.
e-SCM and ERP Integration at Neway: Challenges and Solutions

It is imperative for modern organizations to use ERP and SCM systems to manage their operations. Increasingly, there is a trend to integrate these two systems together as they are found to work in a complementary fashion. But the integration of ERP and SCM systems is usually marked by a number of technical and organizational challenges. The case of Neway discussed in this paper illustrates the process of integration of the ERP and the SCM systems at a modern Chinese organization. Neway used an ERP system that had several shortcomings. To overcome these problems they decided to add an e-SCM system on top of the ERP system. The e-SCM system that was implemented was able to solve the inherent problems of the ERP systems and bring in many benefits that ranged from fostering a paperless environment to efficient inventory tracking and picking to better coordination with vendors and customers. Several challenges were faced in the implementation process and smart solutions were adopted to overcome them.
Hierarchies of keys in secure multicast communications

This work considers key management for secure multicast in the Logical Key Hierarchy (LKH) model and proposes a methodology to establish the minimal key bit length that guarantees a specified degree of confidentiality for the multicast communications managed within this model. We also introduce the concepts of information lifetime and information dependence to formalize the intuition that keys should be longer, and thus stronger, when used to encrypt “important” information, that is information (including other keys) that need to be kept confidential for a longer period. Then, these concepts are used to build a formal theory that is applied to set the correct bit length of every key in the system in such a way to guarantee the prescribed degree of confidentiality of the multicast messages. Quite surprisingly, we formally show that not all the keys in the LKH hierarchy should have the same length; this observation, besides being of theoretical interest, also leads to substantial savings in terms of memory, computation, and bandwidth. The theory we develop to obtain these results can be useful in other contexts as well.
A PAPR reduction technique based on Golay sequences and Fractional Fourier Transform for OFDM systems

High peak-to-average power ratio (PAPR) is the major drawback for multicarrier transmission. In this paper, we propose reliable PAPR reducing method that consists in putting together a low complexity Golay coder followed by special Fractional Fourier Transform (FRFT) block. The input data encoder which provides low order Golay sequences coded modulation is then transmitted through FRFT block. FRFT angles are designed to provide optimal decorrelation between signal and noise. This new method offers competitive performance as well as low complexity compared to previously published methods. Computer simulation for standard WLAN in IEEE 802.11a system shows 30% PAPR improvement and also 25% Bit Error Rate (BER) enhancement by comparison to the competitive methods.
Hagedorn Systems from Backreacted Finite Temperature N_f=2N_c Backgrounds

We present non-extremal generalizations of N_f=2N_c supergravity duals obtained by adding flavor branes to the Chamseddine-Volkov-Maldacena-Nunez background and taking their backreaction into account. For a particular range of parameters we obtain a one parameter family of analytic black hole solutions with regular horizons. These solutions have a temperature that is always above the Hagedorn temperature of little string theory. We find that including flavor branes and their backreaction increases the minimum temperature of these systems from T = 1/(4\pi), for the background without flavor, to T = 1/(2\pi \sqrt{1 + epsilon}), epsilon << 1. We show that outside the range of validity of the analytic solutions presented here the backgrounds have naked singularities.
Dynamic regulation of PGC-1α localization and turnover implicates mitochondrial adaptation in calorie restriction and the stress response

There is increasing evidence that longevity and stress resistance are connected, but the mechanism is unclear. We report that mitochondria are regulated in response to oxidative stress and calorie restriction through a shared mechanism involving peroxisome proliferator‐activated receptor‐γ co‐activator 1α (PGC‐1α). We demonstrate that PGC‐1α subcellular distribution is regulated, and its transcriptional activity is promoted through SIRT1‐dependent nuclear accumulation. In addition, the duration of PGC‐1α activity is regulated by glycogen synthase kinase beta (GSK3β), which targets PGC‐1α for intranuclear proteasomal degradation. This mechanism of regulation permits the rapidity and persistence of PGC‐1α activation to be independently controlled. We provide evidence that this pathway of PGC‐1α regulation occurs in vivo in mice, both in the oxidative stress response and with calorie restriction. Our data show how mitochondrial function may be adapted in response to external stimuli, and support the concept that such adaptation is critically involved in cellular survival and in lifespan extension by calorie restriction.
Adaptive evolution of cryptic coloration: the shape of host plants and dorsal stripes in Timema walking-sticks

The adaptive significance of cryptic colour patterns has seldom been analysed in a phylogenetic context. We mapped data on the presence vs. absence of dorsal stripes, and the use of needle-like vs. broad foliage, onto a recent phylogeny of Timema walking-sticks, in order to infer the evolutionary history of these traits and test the hypothesis that the dorsal stripe is an adaptation for crypsis on needle-like leaves. By maximum parsimony optimization, the dorsal stripe has evolved five or six times in this clade, each time in association with the use of vegetation with needle-like leaves. Maddison’s concentrated changes test showed that this association between morphology and habitat was statistically significant. By contrast, results based on Pagel’s maximum likelihood analyses did not reach significance, probably because the large number of origins of dorsal stripe introduces statistical uncertainty. These results suggest that the adaptations for crypsis can arise readily and in parallel, in the appropriate selective environment. However, they may also constrain the evolution of host-plant use, as there is no unambiguous case of Timema species with dorsal stripes shifting to broad-leaved plants. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society, 2008, 94, 1‐5. ADDITIONAL KEYWORDS: crypsis ‐ host specialization ‐ phylogeny ‐ timematidae.
An Approach Based on Visual Text Mining to Support Categorization and Classification in the Systematic Mapping

Context: Systematic mapping provides an overview of a research area to assess the quantity of evidence existing on a topic of interest. In spite of its relevance, the establishment of consistent categories and classification of primary studies in these categories are manually conducted. Objective: We propose an approach, named SM-VTM (Systematic Mapping based on Visual Text Mining), to support categorization and classification stages in the systematic mapping using Visual Text Mining (VTM), aiming at reducing time and effort required in this process. Method: We established SM-VTM, selected a VTM tool and conducted a case study comparing results of two systematic mappings: one performed manually and another using our approach. Results: The results of both systematic mappings were very similar, showing the viability of SM-VTM. Furthermore, since our approach was applied using a tool, reduction of time and effort can be achieved. Conclusions: The application of VTM seems to be very relevant in the context of systematic mapping.
Transcranial Direct Current Stimulation of the Medial Frontal Cortex Modulates Choise-Induced Preference Changes

Cognitive dissonance arises as a reaction to conflict appearing in choices between two equally attractive options. It leads to changes in the desirability of these options. The chosen option becomes more desirable whereas the rejected option is devalued. Despite cognitive dissonance being largely used by social psychologists to explain social conformity and preference re-evaluation, little is known about the neural mechanisms of such choice-induced preference changes. In this study, we modulated the activity of the posterior medial frontal cortex (pMFC), which has been found to be involved in cognitive dissonance in neuroimaging studies. We influenced the activity of the pMFC before individual choices using both cathodal and anodal transcranial Direct Current Stimulation (tDCS) during a revised version of Brehm's free-choice paradigm. Our results showed that cathodal tDCS over the pMFC significantly decreased the typical choice-induced preference change relative to a sham stimulation. On the contrary, no significant effect of anodal tDCS was observed. Our findings of the influence cathodal tDCS on preference re-evaluation highlight the central contribution of the pMFC in cognitive dissonance and provide evidence that pMFC plays a key role in the implementation of subsequent post-decision preference change.
A parallel fusion method of remote sensing image based on IHS transformation

With the development of modern remote sensing technology, a variety of earth observation satellites could continue to tremendously provide images of different spatial resolution, time resolution, spectral resolution remote sensing, and the remote sensing data obtained is increasing, which forms multi-source image pyramid in the same area. In such cases, in the description of the same object with multiple-source remote sensing information, how to conduct effective data processing, play the advantages of a variety of remote sensing data, the application of remote sensing image fusion is a very important choice. When remote sensing data is large, fusion is large in computing capacity and time-consuming, so it is difficult to carry out rapid, real-time fusion. However, in some remote sensing applications, such as disaster prevention and relief quick, etc., fast even real-time fusion is required. The fusion method based IHS (intensity, hue, saturation) transformation is a basic method. In this paper, with Beijing-1 Micro-satellite remote sensing images as the experimental data, based on the advantages of parallel computing, a parallel IHS fusion method of remote sensing image is proposed. Experiments show that good results of remote sensing image fusion can be obtained with the method, and the fusion speed is also fast.
Double Dissociation of Amygdala and Hippocampal Contributions to Trace and Delay Fear Conditioning

A key finding in studies of the neurobiology of learning memory is that the amygdala is critically involved in Pavlovian fear conditioning. This is well established in delay-cued and contextual fear conditioning; however, surprisingly little is known of the role of the amygdala in trace conditioning. Trace fear conditioning, in which the CS and US are separated in time by a trace interval, requires the hippocampus and prefrontal cortex. It is possible that recruitment of cortical structures by trace conditioning alters the role of the amygdala compared to delay fear conditioning, where the CS and US overlap. To investigate this, we inactivated the amygdala of male C57BL/6 mice with GABA A agonist muscimol prior to 2-pairing trace or delay fear conditioning. Amygdala inactivation produced deficits in contextual and delay conditioning, but had no effect on trace conditioning. As controls, we demonstrate that dorsal hippocampal inactivation produced deficits in trace and contextual, but not delay fear conditioning. Further, pre- and post-training amygdala inactivation disrupted the contextual but the not cued component of trace conditioning, as did muscimol infusion prior to 1- or 4-pairing trace conditioning. These findings demonstrate that insertion of a temporal gap between the CS and US can generate amygdala-independent fear conditioning. We discuss the implications of this surprising finding for current models of the neural circuitry involved in fear conditioning.