Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on data privacy protection algorithm with homomorphism mechanism based on redundant slice technology in wireless sensor networks

Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice technology, this paper proposes the data private protection algorithm with redundancy mechanism, which ensures privacy by privacy homomorphism mechanism and guarantees redundancy by carrying hidden data. Moreover, it selects the routing tree generated by CTP (Collection Tree Protocol) as routing path for data transmission. By dividing at the source node, it adds the hidden information and also the privacy homomorphism. At the same time, the information feedback tree is established between the destination node and the source node. In addition, the destination node immediately sends the packet loss information and the encryption key via the information feedback tree to the source node. As a result, it improves the reliability and privacy of data transmission and ensures the data redundancy.
Geodesic Flow on Polyhedral Surfaces

On a curved surface the front of a point wave evolves in concentric circles which start to overlap and branch after a certain time. This evolution is described by the geodesic flow and helps us to understand the geometry of surfaces. In this paper we compute the evolution of distance circles on polyhedral surfaces and develop a method to visualize the set of circles, their overlapping, branching, and their temporal evolution simultaneously. We consider the evolution as an interfering wave on the surface, and extend isometric texture maps to efficiently handle the branching and overlapping of the wave.
Design, experiences and user preferences for a web-based awareness tool

We describe our experiences with the design, implementation, deployment and evaluation of a Portholes tool which provides group and collaboration awareness through the Web. The research objective was to explore as to how such a system would improve communication and facilitate a shared understanding among distributed development groups. During the deployment of our Portholes system, we conducted a naturalistic study by soliciting user feedback and evolving the system in response. Many of the initial reactions of potential users indicated that our system projected the wrong image so that we designed a new version that provided explicit cues about being in public and who is looking back to suggest a social rather than information interface. We implemented the new design as a Java applet and evaluated design choices with a preference study. Our experiences with different Portholes versions and user reactions to them provide insights for designing awareness tools beyond Portholes systems. Our approach is for the studies to guide and to provide feedback for the design and technical development of our system.
A Study of Hidden Markov Models for O-line Recognition of Handwritten Characters

In many pattern recognition problems, research has shown that signicant improvement in the overall performance may be achieved through a combination of multiple classiers, rather than a further development of a single classier (1). As a process, among others, to reduce the amount of information to be treated by the classier, one can represent a character image thanks to a sequence of straight lines. Hidden Markov Models (HMMs) are then appropriate to deal with the variability of the length of these sequences, and the one of the relative position of a given segment in the feature vector. Application of HMMs has led us to dene an average character for each class, and to con- sider some new parameters, such as the probability of the length of a sequence, and the probabilities that a sequence ends on, or comes through, a given state. The recognition rate obtained on the NIST3 database (2) is of 84.4 %, using one HMM per class, and of 86 %, using up to 3 HMMs per class. Finally, the association of these HMMs with a neural net- work trained on the digital images of the characters and oering a recognition rate of 93.1 %, allows to reach an overall recognition rate of 94.1 %.
Incremental emotion recognition

Most emotion recognition systems do not perform real-time emotion recognition due to latencies caused by phrase segmentation and resource-intensive feature acquisition, etc. To address this issue, we present an emotion recognition approach that can estimate speaker emotions with much lower latency. The proposed approach does not rely on phrase-level features to recognize speaker emotion; rather, it estimates the speaker’s emotional state over the course of the utterance incrementally, using a shifting n-word window on the basis of easily computable features. These features are obtained from three information streams, i.e. cepstral, prosodic and textual, at the wordlevel and combined at decision-level using a statistical framework. Our work shows that combining the three information streams yields higher emotion recognition accuracy than any single information stream. Using features extracted from n-word sequences rather than phrases provides for the low-latency capabilities of the proposed system, without any loss in utterance-level emotion recognition accuracy. The performance of the proposed system on a binary utterance-level emotion recognition task using an in-house database shows a relative improvement of 41% over chance, compared to a relative improvement of 31.82% shown by the baseline phrase-level emotion recognition approach.
GS textplorer -: adaptive framework for information retrieval

TheWEBSOM is a method developed originally at Helsinki University of Technology for analyzing and visualizing large document collections [1, 4]. In the WEBSOM method, the self-organizing map algorithm [3] is used to automatically organize collections of documents on a map to enable easy exploration and search of the collection. Map regions that are close to each other contain similar documents. The main objectives in developing the WEBSOM method has been to o er a method for exploring text collections that is di erent from the queryresult approach, enabling the user to get an overall view to the document collection. Moreover, there are no principled limits on the type of text material that the method can handle.
FLOOD HAZARD ASSESSMENT USING PANCHROMATIC SATELLITE IMAGERY

Panchromatic (PAN) satellite imagery has been used successfully in different applications such as topographic mapping and terrain modelling. Nevertheless, PAN imagery is still one of the less used digital sources for land-change studies except few processes where the high-resolution of the PAN images is used to improve the visualization quality of the multi-spectral images. This research aimed to study and facilitate the use of the PAN satellite imagery in image classification for flood hazard assessment application. In particular, this research is investigating the potential use of PAN satellite imagery for flood hazard assessment of one of the high floods of the Nile River occurred in Year 1998. Several existing techniques and approaches used for digital image processing were examined and assessed for PAN image classification. The study focuses on four different approaches that could be used for PAN image classification and flood hazard assessment: a) image interpretation, b) edge detection, c) pixel-based image classification, and d) texture analysis (TEX). The described approaches were investigated for PAN satellite imagery covering a part of the Nile Valley in Egypt. Two SPOT PAN satellite images covering part of the Nile Valley in Egypt before and after the 1997/1998 Nile flood have been utilized. Different areas of interest (islands, coastal areas, etc.) have been identified to study the efficiency of the previous classification approaches for PAN image classification. The results of four PAN image classification approaches are presented and assessed for the study sites using area and sample comparative analysis. The results revealed that Contextual Classifier on PAN imagery and Maximum Likelihood Classifier on TEX imagery offer the nearest estimation of flooding areas. The study shows high integrity of the tested approaches for PAN image classification and accuracy comparable to conventional signature-based classification technique of multi-spectral images could generally be achieved.
Incentives, wages, and promotions: theory and evidence

I study an incentive problem that has been largely ignored in the agency literature: incentives for repeated (human capital) investment. The optimal contract is very simple but still provides rich implications for incentive and wage structures in large organizations. The empirical evidence is presented using personnel records of health insurance claim processors in a large U.S. insurance company. These processors are white-collar, nonmanagerial, female, service industry workers - a rapidly growing but rarely studied labor group. The empirical findings are consistent with the main features of the optimal contract.
Effect of Verb Subdivision and Noun Incorporation on Dependency Parsing

Parsing based on tree bank is a central issue of current natural language processing. The machine learning method of SVM and the dependency tree bank of HIT-IR-CDT is adopted in this work. In order to increase the parsing accuracy by linguistic means, verb subdivision and noun incorporation is done. The result shows, after verb subdivision, the accuracy of unlabeled attachment score increases from 79.05% to 79.5%, and the accuracy of labeled attachment score increases from 76.38% to 76.81%. Noun incorporation has little effect on the accuracy of dependency analysis but it can reduce training time efficiently.
CONVOLUTION FILTERING AND MATHEMATICAL MORPHOLOGY ON AN IM AGE: A UNIFIED VIEW

A burner ( 1 ), fed with a mixture of fuel and air, comprising a body ( 2 ) consisting of two half-shells having a plane of symmetry, inside said body a Venturi tube ( 3 ) being defined, provided with and inlet ( 4 ) into which said mixture of fuel and air is introduced, said Venturi tube ( 3 ) communicating upwardly with a distributing chamber ( 5 ) of said mixture of fuel and air, said distributing chamber ( 5 ) communicating upwardly with a burner head ( 8 ), closed upwardly by a diffuser ( 9 ), on which openings are made for the outflow and the subsequent combustion of said mixture of fuel and air, said distributing chamber ( 5 ) exhibits a first portion ( 6 ) and a second portion ( 7 ), said first portion ( 6 ), communicating with said Venturi tube, comprising at least a first stretch ( 6 a; 6 c; 6 e) having a cross section of substantially constant width and at least a second stretch ( 6 b; 6 d) having cross section that increases progressively along a direction parallel to said plane of symmetry towards an end of said distributing chamber ( 5 ) faraway with respect to said Venturi tube ( 3 ), said second portion ( 7 ) having a section that increases progressively towards said burner head ( 8 ).
Parameter learning for improving binary descriptor matching

Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and are comparable to SIFT.
Low power self-timed Radix-2 division (poster session)

A self-timed radix-2 division scheme for low power consumption is proposed. By replacing dual-rail dynamic circuits in non-critical data paths with single-rail static circuits, power dissipation is decreased, yet performance is maintained by speculative remainder computation. SPICE simulation results show that the proposed design can achieve 33.8-ns latency for 56-bit mantissa division and 47% energy reduction compared to afully dual-rail version.
Design and development of a soft robot with crawling and grasping capabilities

This paper describes the design and development of a robot with six soft limbs, with the dual capability of pushing-based locomotion and grasping by wrapping around objects. Specifically, a central platform lodges six silicone limbs, radially distributed, with cables embedded. A new mechanism-specific gait, invariant regarding the number of limbs, has been implemented. Functionally, some limbs provide stability while others push and pull the robot to locomote in the desired direction. Once the robot is close to a target, one limb is elected to wrap around the object and, thanks to the particular limb structure and the soft material, a friction-based grasping is achieved. The robot is inspired by the octopus and implements the key principles of locomotion in this animal, without coping the full body structure. For this reason it works in water, but it is not restricted to this environment. The experiments show the effectiveness of the original solution in locomotion and grasping.
Slowing the Firehose: Multi-Dimensional Diversity on Social Post Streams

Web 2.0 users conveniently consume content through subscribing to content generators such as Twitter users or news agencies. However, given the number of subscriptions and the rate of the subscription streams, users suffer from the information overload problem. To address this issue, we propose a novel and flexible diversification paradigm to prune redundant posts from a collection of streams. A key novelty of our diversification model is that it holistically incorporates three important dimensions of social posts, namely content, time and author. We show how different applications, such as microblogging, news or bibliographic services, require different settings for these three dimensions. Further, each dimension poses unique performance challenges towards scaling the diversification model for many users and many high-throughput streams. We show that hash-based content distance measures and graph-based author distance measures are both effective and efficient for social posts. We propose scalable real-time stream processing algorithms leveraging efficient indexes that input a social post stream and output a diversified version of the stream, diversified across all three dimensions. Next, we show how these techniques can be extended to serve multiple users by appropriately reusing indexing and computation where possible. Through extensive experiments on real Twitter data, we show that our diversification model is effective and our solutions are scalable. We show that different algorithms perform best for different application settings.
GRATIS: Free Bits In The Network ; CU-CS-1075-10

Conventional signal-to-noise (SNR) based rate adaptation algorithms in wireless networks provide limited flexibility in terms of the maximum achievable data rates. Transmitters are often forced to step down to a lower data rate due to the lack of an intermediate rate despite having a higher SNR than the minimum required for the lower rate. In this paper, we propose a novel scheme that provides increased flexibility to the MAC layer by providing multiple step down rates, while utilizing client channel diversity to transmit independent packets to multiple users: this is an example of simultaneous transmission. We achieve this by having a programmable baseband modulator producing constellations that provide increased network throughput while maintaining acceptable error rates. We call this scheme Group Rate Adaptation and Transmission with Intertwined Symbols, or GRATIS for short. We have implemented this method using a testbed of reconfigurable radio nodes and demonstrated that this method improves average throughput while maintaining similar or lower packet loss compared to the conventional contention based transmission mechanism as well as other multiuser techniques such as superposition coding and hierarchical modulation.
Cyclosporine A Impairs the Macrophage Reverse Cholesterol Transport in Mice by Reducing Sterol Fecal Excretion

Despite the efficacy in reducing acute rejection events in organ transplanted subjects, long term therapy with cyclosporine A is associated with increased atherosclerotic cardiovascular morbidity. We studied whether this drug affects the antiatherogenic process of the reverse cholesterol transport from macrophages in vivo. Cyclosporine A 50 mg/kg/d was administered to C57BL/6 mice by subcutaneous injection for 14 days. Macrophage reverse cholesterol transport was assessed by following [3H]-cholesterol mobilization from pre-labeled intraperitoneally injected macrophages, expressing or not apolipoprotein E, to plasma, liver and feces. The pharmacological treatment significantly reduced the amount of radioactive sterols in the feces, independently on the expression of apolipoprotein E in the macrophages injected into recipient mice and in absence of changes of plasma levels of high density lipoprotein-cholesterol. Gene expression analysis revealed that cyclosporine A inhibited the hepatic levels of cholesterol 7-alpha-hydroxylase, concomitantly with the increase in hepatic and intestinal expression of ATP Binding Cassette G5. However, the in vivo relevance of the last observation was challenged by the demonstration that mice treated or not with cyclosporine A showed the same levels of circulating beta-sitosterol. These results indicate that treatment of mice with cyclosporine A impaired the macrophage reverse cholesterol transport by reducing fecal sterol excretion, possibly through the inhibition of cholesterol 7-alpha-hydroxylase expression. The current observation may provide a potential mechanism for the high incidence of atherosclerotic coronary artery disease following the immunosuppressant therapy in organ transplanted recipients.
Double scaling limits in gauge theories and matrix models

We show that = 1 gauge theories with an adjoint chiral multiplet admit a wide class of large-N double-scaling limits where N is taken to infinity in a way coordinated with a tuning of the bare superpotential. The tuning is such that the theory is near an Argyres-Douglas-type singularity where a set of non-local dibaryons becomes massless in conjunction with a set of confining strings becoming tensionless. The doubly-scaled theory consists of two decoupled sectors, one whose spectrum and interactions follow the usual large-N scaling whilst the other has light states of fixed mass in the large-N limit which subvert the usual large-N scaling and lead to an interacting theory in the limit. F-term properties of this interacting sector can be calculated using a Dijkgraaf-Vafa matrix model and in this context the double-scaling limit is precisely the kind investigated in the ``old matrix model'' to describe two-dimensional gravity coupled to c < 1 conformal field theories. In particular, the old matrix model double-scaling limit describes a sector of a gauge theory with a mass gap and light meson-like composite states, the approximate Goldstone boson of superconformal invariance, with a mass which is fixed in the double-scaling limit. Consequently, the gravitational F-terms in these cases satisfy the string equation of the KdV hierarchy.
Design and Implementation of Parallel Branches for Concatenated BCH and LDPC Coding on FPGA

Recently, satellite services applications are of wide utilization as in radio and television broadcast, voice and data communication, and location services communication. Error correction codes are continuously designed to accomplish reliable communication with low Bit-Error-Rate (BER) in satellite communication link. Modern techniques are using concatenation of different codes, to achieve low BER. This paper presents an implementation for Bose-Chaudauri Hocquenghem (BCH) on FPGA. Also, it provides an implementation for low density parity check (LDPC) on FPGA. Finally, a design and implementation for multiple branches of BCH concatenated with LDPC on FPGA, where the encoder is designed such that two parallel BCH branches are concatenated with one LDPC, while the decoder design is four parallel BCH branches concatenated with one LDPC. This proposed design improves the link reliability and increases the throughput when compared with the serial concatenated coding systems, where the encoder throughput increased from 100 Mbps to 200 Mbps, and the decoder enhanced from 10.5 Mbps to 50 Mbps.
Predicting Popularity of Twitter Accounts through the Discovery of Link-Propagating Early Adopters

In this paper, we propose a method of ranking recently created Twitter accounts according to their prospective popularity. Early detection of new promising accounts is useful for trend prediction, viral marketing, user recommendation, and so on. New accounts are, however, difficult to evaluate because they have not yet established the reputation they deserve, and we cannot apply existing link-based or other popularity-based account evaluation methods. Our method first finds early adopters, i.e., users who often find new good information sources earlier than others. Our method then regards new accounts followed by good early adopters as promising, even if they do not have many followers now. In order to find good early adopters, we estimate the frequency of link propagation from each account, i.e., how many times the follow links from the account have been copied by its followers. If the frequency is high, the account must be a good early adopter who often find good information sources earlier than its followers. We develop a method of inferring which links are created by copying which links. One important advantage of our method is that our method only uses information that can be easily obtained only by crawling neighbors of the target accounts in the current Twitter graph. We evaluated our method by an experiment on Twitter data. We chose then-new accounts from an old snapshot of Twitter, compute their ranking by our method, and compare it with the ranking based on the number of followers the accounts currently have. The result shows that our method produces better rankings than various baseline methods, especially for very new accounts that have only a few followers.
Characterization of tin selenides synthesized by high-energy milling

Tin selenides SnSeX (x=1,2) were synthesized from tin and selenium powder precursors by high-energy milling in the planetary ball mill Pulverisette 6 (Fritsch, Germany). The orthorhombic tin selenide SnSe and the hexagonal tin diselenide SnSe2 phases were formed after 4 min and 5 min of milling, respectively. Specific surface area of both selenides increased with increasing time of mechanochemical synthesis. The particle size distribution analysis demonstrated that the synthesized products contain agglomerated selenide particles consisting of numerous idiomorphic tin selenide crystals, measuring from 2 to more than 100 nm in diameter, which were also documented by TEM. UV-Vis spectrophotometry confirmed that tin selenide particles do not behave as quantum dots.