Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data Mining Perspective in Privacy Preserving Data Mining Systems

Privacy Preserving Data Mining ( ) presents a novel framework for extracting and deriving information when the data is distributed amongst the multiple parties. The privacy preservation of data and the use of efficient data mining algorithms in systems is a major issue that exists. Most of the existing systems employ the cryptographic key exchange process and the key computation process accomplished by means of certain trusted server or a third party. To eliminate the key exchange and key computation overheads this paper discusses the Key Distribution-Less Privacy Preserving Data Mining ( ) system. The novelty of the system is that no data is published but only the association rules are published to achieve effective data mining results. The embodies the data mining algorithm for classification rule generation and data mining. The results discussed in this paper compare the based system with the based system and the efficiency in rule generation, overhead reduction and classification efficiency of the latter is proved.
Architectural issues in network-centric computing

The widespread use of the Internet has led to great changes in traditional computational models. The concept of the network-centric computing is becoming more and more popular. Software architecture, as an emerging discipline, focuses on the high-level structures of large complex software systems. With the critical challenges from the new computational model, many open issues of software architectures emerge. In this paper, we present some requisite technologies that are still not completely settled and offer some suggestions based on a survey of relevant study experience to date.
Scene: using scenario diagrams and active text for illustrating object-oriented programs

Scenario diagrams are a well-known notation for visualizing the message flow in object-oriented systems. Traditionally, they are used in the analysis and design phases of software development to prototype the expected behavior of a system. We show how they can be used in reverse for understanding and browsing existing software. We have implemented a tool called Scene (SCENario Environment) that automatically produces scenario diagrams for existing object-oriented systems. The tool makes extensive use of an active text framework providing the basis for various hypertext-like facilities. It allows the user to browse not only scenarios but also various kinds of associated documents, such as source code (method definitions and calls), class interfaces, class diagrams and call matrices.
The City and Grassroots

A versatile multi-size adaptor to be connected to a duct in a hot or cold air distribution system and having a box-like air discharge outlet in which an air diffuser can be mounted, and having an air inlet made in stepped sections of different size for enabling the same to be connected to one of several sizes of ducts to suit installation requirements A modified embodiment includes a bellows section between the air outlet and the stepped air inlet that can be axially compressed, or extended, and/or flexed so that the stepped inlet can be connected to a duct disposed at an angle to the outlet.
Species Invasions from Commerce in Live Aquatic Organisms: Problems and Possible Solutions

ABSTRACT In the Laurentian Great Lakes region, commercial activities involving live fish bait, horticultural and water-garden plants, biological supplies, pets, and live food are the principal pathways for intentional introductions of live aquatic organisms. We sampled species for sale in these trades and found that the risks of new invasions and of spreading known invaders are high. Moreover, most animals were identified by common name only, and even though scientific names were more often applied to plants, consumers cannot be certain what species they are receiving because misidentification is common. Finally, 90 percent of plant orders arrived contaminated with unordered live organisms. The policy goal of US and Canadian national and state or provincial agencies is to reduce the risk of harmful introductions. Our results demonstrate that meeting this goal will require accurate identification of species by vendors, the removal of known and likely invasive species from trade, and reductions in the number of contaminant organisms.
Traceroute-Based Topology Inference without Network Coordinate Estimation

Underlay topology information is important to construct efficient overlay networks. To achieve end-to-end network topology inference among a group of hosts, traceroute-like tools are often used. Previously, Max-Delta has been proposed to infer a highly accurate topology with a low number of traceroutes. However, Max-Delta relies on external tools to estimate host coordinates, which incur considerable deployment overhead. In this paper, we consider novel inference schemes with no coordinate estimation. One choice is to select long paths to traceroute. That is, based on existing traceroute results, each host can estimate the distance between another host and itself. It can then select the host with the largest distance between them as the traceroute target. We call this scheme Longest-Path-First (LPF). Similarly, we can define Shortest-Path-First (SPF) inference. The intuition may indicate that LPF performs better than SPF, as longer paths often contain more underlay links and routers, and hence more undiscovered links or routers. However, our simulation results on Internet-like topologies show that SPF can achieve comparable performance with Max-Delta, while LPF performs even worse than a random inference scheme. To explain the results, we analyze the statistics of all-pairs paths between hosts. Our results show that long paths have serious overlaps on underlay links, showing much higher path stress than short paths. We also find that there exist quite a few links only appearing in short paths and seldom appearing in long paths. Therefore, with the same number of traceroutes, SPF can discover more underlay links and routers than LPF. Furthermore, as SPF prefers short paths, a traceroute in SPF sends less probing packets and consumes less network resource than that in LPF. Therefore, SPF is a highly efficient inference scheme with low deployment overhead and low measurement overhead.
Effective keyword search for valuable lcas over xml documents

In this paper, we study the problem of effective keyword search over XML documents. We begin by introducing the notion of Valuable Lowest Common Ancestor (VLCA) to accurately and effectively answer keyword queries over XML documents. We then propose the concept of Compact VLCA (CVLCA) and compute the meaningful compact connected trees rooted as CVLCAs as the answers of keyword queries. To efficiently compute CVLCAs, we devise an effective optimization strategy for speeding up the computation, and exploit the key properties of CVLCA in the design of the stack-based algorithm for answering keyword queries. We have conducted an extensive experimental study and the experimental results show that our proposed approach achieves both high efficiency and effectiveness when compared with existing proposals.
Improving non-native mispronunciation detection and enriching diagnostic feedback with DNN-based speech attribute modeling

We propose the use of speech attributes, such as voicing and aspiration, to address two key research issues in computer assisted pronunciation training (CAPT) for L2 learners, namely detecting mispronunciation and providing diagnostic feedback. To improve the performance we focus on mispronunciations occurred at the segmental and sub-segmental levels. In this study, speech attributes scores are first used to measure the pronunciation quality at a sub-segmental level, such as manner and place of articulation. These speech attribute scores are integrated by neural network classifiers to generate segmental pronunciation scores. Compared with the conventional phone-based GOP (Goodness of Pronunciation) system we implement with our dataset, the proposed framework reduces the equal error rate by 8.78% relative. Moreover, it attains comparable results to phone-based classifier approach to mispronunciation detection while providing comprehensive feedback, including segmental and sub-segmental diagnostic information, to help L2 learners.
A Metalanguage for interactive proof in LCF

LCF (Icqic for Caqmtable Functions) is a prcof generating system mnsisting of an interactive programing language MG (MetaLmguage) for mnducting prcofs in PPA (Polynmrphic Predicate A-calculus) , a deductive calculus suitable for the formalisation of reasoning almut recursively defined functions, in particular about the syntax, semantics and iq? lementations of many prqrcmming languages. PPI is an enrichment (in respect of type structure and expressive pcwer) of an extended a-calculus due to Dana Scott and is fully discussed elsewhere [ 22 I . The puxposes of this paper are (a) to illustrate the features of ML which me it of general interest in language design quite independently of its use for machine assistd formal pxcof, (b) to illustrate ML applied to PPA, in encoding interesting prcof -finding-andperfonning procedures, and (c) to convey a methodology for controlled semiautomatic proof.
Performance evaluation of inter-cell interference of LTE-A system using carrier aggregation and CoMP techniques

Long Term Evolution Advanced (LTE-A) is a promising technology to achieve peak data rate of up to 1 Gbit/s in the downlink and 500 Mbit/s in the uplink. Because adjacent cells employs the same frequency in universal frequency reuse scheme, interference between adjacent cells may degrade the bit rate, particularly at the cell edges. Inter-Cell Interference Coordination (ICIC) based on carrier aggregation as well as Coordinated Multipoint (CoMP) transmission and reception techniques have been considered as promising techniques to overcome the interference problem and to improve the bit rate at the cell edges. ICI problem can be mitigated by using different arrangements of frequency-bands allocations and possibly with different transmission power, distinguishing between the cell centre and the cell edge users. In this paper, mitigation of inter-cell interference using carrier aggregation (CA) and coordinated multipoint (CoMP) techniques are evaluated through computer simulaton. The simulation employs different frequency allocation schemes, fractional frequency reuse, different transmission powers, and different radio resource scheduling algorithms to evaluate and to enhance cell edge performance, bandwidth efficiency and throughput. The performance is evaluated in terms of SINR as a function of the number of femtocell users, and the separation distance from interfering cells. The performance is then compared between conventional technique, CA technique and the combined CA and CoMP techniques.
High Resistance Open localization

Magnetic Field Imaging (MFI) is a widely used technique in the Semiconductor industry for non-destructive Electrical Fault Isolation (EFI) for shorts, leakages and opens. When the devices shrink and the mobile electronics industry has a more strict criteria for speed with low power consumption, making High Resistance Opens (HRO) failures more important to detect. We show in this paper two cases in which magnetic field phase analysis using MFI helped localize HRO failures that were causing delays and slower speed in addition to higher power consumption.
Indicator Variables for Optimal Policy Under Asymmetric Information

The optimal weights on indicators in models with partial information about the state of the economy and forward-looking variables are derived and interpreted, both for equilibria under discretion and under commitment. The private sector is assumed to have information about the state of the economy that the policymaker does not possess. Certainty-equivalence is shown to apply, in the sense that optimal policy reactions to optimally estimated states of the economy are independent of the degree of uncertainty. The usual separation principle does not hold, since the estimation of the state of the economy is not independent of optimization and is in general quite complex. We present a general characterization of optimal filtering and control in settings of this kind, and discuss an application of our methods to the problem of the optimal use of 'real-time' macroeconomic data in the conduct of monetary policy.
Trading Anonymity for Influence in Open Communities Voting Schemata

We discuss how the notion of gradual information disclosure can be incorporated into basic social computing protocols, such as reputation-based service selection. Our technique links the decrease in anonymity a subject is ready to endure (e.g., by disclosing the values of some of her attributes) to the weight her views will carry in the final outcome of the protocol.
Multiple integer packing for optimizing partial homomorphic encrypted databases

Database compromises are increasing nowadays, and database encryption is gaining its importance. Database encryption increases the size of the data, and time for processing the data. Optimizing the underlying encryption schemes helps to improve the performance of the encrypted databases. Processing queries over encrypted data, without decrypting it remained a holy grail until CryptDB. CryptDB is the first practical system which supports almost all queries over encrypted database. It leverages partial homomorphic encryption and SQL-Aware encryption scheme for encrypted query processing. CryptDB also suffers from storage and time issues. We propose packing of integers into a single field as a solution to reduce space issue of CryptDB. Any system which uses additive homomorphic schemes for encryption can adopt this method for storage optimization.
Truly Sub-cubic Algorithms for Language Edit Distance and RNA-Folding via Fast Bounded-Difference Min-Plus Product

It is a major open problem whether the (min,+)-product of two n by n matrices has a truly sub-cubic time algorithm, as it is equivalent to the famous All-Pairs-Shortest-Paths problem (APSP) in n-vertex graphs. There are some restrictions of the (min,+)-product to special types of matrices that admit truly sub-cubic algorithms, each giving rise to a special case of APSP that can be solved faster. In this paper we consider a new, different and powerful restriction in which one matrix can be arbitrary, as long as the other matrix has "bounded differences" in either its columns or rows, i.e. any two consecutive entries differ by only a small amount. We obtain the first truly sub-cubic algorithm for this Bounded Differences (min,+)-product (answering an open problem of Chan and Lewenstein). Our new algorithm, combined with a strengthening of an approach of L. Valiant for solving context-free grammar parsing with matrix multiplication, yields the first truly sub-cubic algorithms for the following problems: Language Edit Distance (a major problem in the parsing community), RNA-folding (a major problem in bioinformatics) and Optimum Stack Generation (answering an open problem of Tarjan).
Smartphone User Interface

Touch-based operations are becoming increasingly commonplace as smartphone use continues to expand. Compared to indirect keypad operations on conventional feature phones, direct operations on a touchscreen make for smooth use of mobile phones without having to learn numerous operation rules and procedures. This article presents Fujitsu’s approach to achieving a more intuitive user interface that enhances the direct-operation learning effect and enables even users who are not especially adept at mobile-phone operations to use a smartphone without having to refer to a manual. It also introduces a character input system using Fujitsu’s handwriting recognition technology as an example of exploiting touchscreen features.
Controlled and Adaptive Mesh Zippering

Merging meshes is a recurrent need in geometry modeling and it is a critical step in the 3D acquisition pipeline, where it is used for building a single mesh from several range scans. A pioneering simple and effective solution to merging is represented by the Zippering algorithm (Turk and Levoy, 1994), which consists of simply stitching the meshes together along their borders. In this paper we propose a new extended version of the zippering algorithm that enables the user to control the resulting mesh by introducing quality criteria in the selection of redundant data, and allows to zip together meshes with different granularity by an ad hoc refinement algorithm.
Adslot Mining for Online Display Ads

Finding appropriate adslots to display ads is an important step to achieve high conversion rates in online display advertising. Previous work on ad recommendation and conversion prediction often focuses on matching between adslots, users and ads simultaneously for each impression at micro level. Such methods require rich attributes of users, ads and adslots, which might not always be available, especially with ad-adslot pairs that have never been displayed. In this research, we propose a macro approach for mining new adslots for each ad by recommending appropriate adslots to the ad. The proposed method does not require any user information and can be pre-calculated offline, even when there are not any impressions of the ad on the target adslots. It applies matrix factorization techniques to the ad-adslot performance history matrix to calculate the predicted performance of the target adslots. Experiments show that the proposed method achieves a small root mean-square error (RMSE) when testing with offline data and it yields high conversion rates in online tests with real-world ad campaigns.
A GPU-based associative memory using sparse Neural Networks

Associative memories, serving as building blocks for a variety of algorithms, store content in such a way that it can be later retrieved by probing the memory with a small portion of it, rather than with an address as in more traditional memories. Recently, Gripon and Berrou have introduced a novel construction which builds on ideas from the theory of error correcting codes, greatly outperforming the celebrated Hopfield Neural Networks in terms of the number of stored messages per neuron and the number of stored bits per synapse. The work of Gripon and Berrou proposes two retrieval rules, SUM-OF-SUM and SUM-OF-MAX. In this paper, we implement both rules on a general purpose graphical processing unit (GPU). SUM-OF-SUM uses only matrix-vector multiplication and is easily implemented on the GPU, whereas SUM-OF-MAX, which involves non-linear operations, is much less straightforward to fulfill. However, SUM-OF-MAX gives significantly better retrieval error rates. We propose a hybrid scheme tailored for implementation on a GPU which achieves a 880-fold speedup without sacrificing any accuracy.
Using a Genetic Algorithm to Control Randomized Unit Testing

Randomized testing has been shown to be an effective method for testing software units. However, the thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. In this paper, we describe a system which uses a genetic algorithm to find parameters for randomized unit testing that optimize test coverage. We compare our coverage results to previous work, and report on case studies and experiments on system options. In order to optimize the system, we used data mining techniques to analyze which genes were the most useful. We also report on the results of this analysis and optimization.
Accuracy and Timeliness in ML Based Activity Recognition

While recent Machine Learning (ML) based techniques for activity recognition show great promise, there remain a number of questions with respect to the relative merits of these techniques. To provide a better understanding of the relative strengths of contemporary Activity Recognition methods, in this paper we present a comparative analysis of Hidden Markov Model, Bayesian, and Support Vector Machine based human activity recognition models. The study builds on both pre-existing and newly annotated data which includes interleaved activities. Results demonstrate that while Support Vector Machine based techniques perform well for all data sets considered, simple representations of sensor histories regularly outperform more complex count based models.
Repayment Capacity Sensitivity Analysis Using Purdue Farm Financial Analysis Spreadsheet

Many agricultural producers purchased capital items the past few years and some used borrowed funds to finance the purchase. The principal payments on those term loans are paid from net farm income. This paper discusses the sensitivity of farm loan repayment capacity to changes in the gross revenue and operating expenses that determine net farm income. Sensitivity analysis is conducted using the Purdue Farm Financial Analysis Spreadsheet. The sensitivity analysis and application of the updated program are illustrated using a case study
Anterior Cruciate Ligament Reconstruction and Osteoarthritis

Objective: The goals of this study are to address several questions, the answers to which are key to the understanding and eventually to the prevention of this frequent source of morbidity. These questions include the following: (1) What is the natural history of anterior cruciate ligament (ACL) deficiency? (2) How important is the status of the meniscus at the time of reconstruction? (3) Does ACL reconstruction prevent the development of osteoarthritis in the long term? (4) Can we predict which patients will develop osteoarthritis? (5) What can be done? Design: This study addresses the key questions above through the long-term follow-up of a cohort of patients treated with ACL reconstruction by Professor Henri Dejour in Lyon, France, supplemented with a review of the relevant literature. Results: The prevalence of osteoarthritis in ACL-deficient knees is about 40% after 15 years and close to 90% after 25 to 35 years. It remains unclear whether reconstruction of the ACL significantly reduces this risk. The status of the meniscus at the time of ACL reconstruction is a strong predictor of the risk of osteoarthritis: Patients who undergo total meniscectomy are at 2- to 10-fold increased risk of developing osteoarthritis relative to those with intact menisci. Patients showing early evidence of arthritis at short- to medium-term follow-up are at high risk for progression over subsequent years. Numerous emerging techniques may provide tools to more effectively prevent and treat osteoarthritis following ACL injury in the future. Conclusion: Osteoarthritis following ACL injury continues to be a major problem requiring further research.