Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Deductive Verification of Message-Passing Parallel Programs

Program verification techniques based on deductive reasoning can provide a very high level of assurance of correctness. These techniques are capable of proving correctness without placing artificial bounds on program parameters or on the sizes of inputs. While there are a number of mature frameworks for deductive verification of sequential programs, there is much less for parallel programs, and very little for message-passing. We propose a method for the deductive verification of message-passing programs that involves transforming the program into an annotated sequential program that can be verified with off-the-shelf deductive tools, such as Frama-C. The method can prove user-specified correctness properties without any bounds on the number of processes or other parameters. We illustrate this method on a toy example, and analyze its strengths and weaknesses.
Word Segmentation From Phoneme Sequences Based On Pitman-Yor Semi-Markov Model Exploiting Subword Information

Word segmentation from phoneme sequences is essential to identify unknown words -of-vocabulary; OOV) in spoken dialogues. The Pitman-Yor semi-Markov model (PYSMM) is used for word segmentation that handles dynamic increase in vocabularies. The obtained vocabularies, however, still include meaningless entries due to insufficient cues for phoneme sequences. We focus here on using subword information to capture patterns as “words. ” We propose 1) a model based on subword N-gram and subword estimation using a vocabulary set, and 2) posterior fusion of the results of a PYSMM and our model to take advantage of both. Our experiments showed 1) the potential of using subword information for OOV acquisition, and 2) that our method outperformed the PYSMM by 1.53 and 1.07 in terms of the F-measure of the obtained OOV set for English and Japanese corpora, respectively.
A Novel Full Hydrodynamic Focusing Design in Flow Cytometer

Hydrodynamic focusing, among various physical mechanisms used in flow cytometers, has proven to be the most effective technique used for aligning sample stream into a single film without rupturing or changing the characteristics of samples. In this paper, a new micro channel design for focusing the sample flow in all directions is presented. Using proposed design, multidimensional hydrodynamic focusing is achieved using conical shaped sheath side channel located around a cylindrical shaped channel containing sample stream. Numerical simulation is performed using COMSOL multi-physics software to explore the effect of several parameters on shape and value of focused stream width. A comparison between the impact of the proposed design and a previous one on the focused stream width is presented to show the efficiency of the model. Furthermore, the effect of multiple hydrodynamic focusing is examined.
Comparative Experimental Evaluation of Zero-Voltage-Switching Si Inverters and Hard-Switching Si and SiC Inverters

Several efforts have been put in the reduction of the power losses of dc–ac converters by investigating soft-switching topologies, which have been reported to boost efficiencies of silicon inverters up to 99%. However, the auxiliary circuits of the switches significantly impact on the circuit complexity. Conversely, the use of wide bandgap devices offers an alternative for the reduction of the losses, while keeping the simple structure of a traditional inverter. This paper aims to experimentally evaluate the performance of four prototypes in terms of efficiency and costs. Two zero-voltage switching inverters, differing only in the power switches, and two comparable hard-switching inverters, mounting both silicon and silicon carbide devices, have been built and compared. Overall, the soft-switching inverters allow improving the efficiency at high output current in comparison with hard-switching Si inverters, but their efficiency is comparable to that of silicon carbide (SiC) inverters or even lower at low output currents. The economic analysis has shown that the cost of the power stage of the soft-switching inverters and the hard-switching SiC inverter is about 2.5 and 1.5 times that of the hard-switching inverter based on Si insulated gate bipolar transistors, respectively.
Multi-slot and Multi-user Joint Detection Scheme for CRDSA in Satellite Network

: A multi-slot and multi-user detection scheme is presented for the contention resolution diversity slotted ALOHA (CRDSA) in satellite networks. The scheme can eﬀectively resolve the deadlock loop of the collided packets of J users in J slots, and can be applied to DVB-RCS2 directly with a little change on receiver. The simulation results show the throughput of the CRDSA with the proposed scheme can improve greatly when the PLR is at 0.001, and has less latency compared with the conventional CRDSA.
The Impact of Spam Reviews on Feature-based Sentiment Analysis

With the explosive growth of online social media, people can convey their experience and share with others on a common platform. People’s opinions and experience became the most important source of information in the process of sentiment analysis for making decisions. The performance of opinion mining depends on the availability of trustworthy opinions for sentiment analysis. Unfortunately, spammers write spam reviews which can be positive or negative opinions in order to promote their services or damage the reputation of their competitors’ services. Spam reviews may mislead potential customers and affect their experience and influence their ideas. These spam reviews must be identified and removed to avoid possible deceitful customers. The main objective of this paper is to present an enhanced feature-based sentiment analysis algorithm that improves the performance of sentiment classification. The proposed algorithm is developed to assign accurate sentiment score to each feature in social reviews by considering spam reviews detection. The proposed algorithm also examines the effect of three different feature extraction methods on the performance of sentiment classification. Finally, the results indicate that the proposed algorithm achieves an accuracy of about 79.56% in classifying opinions.
Extracting Addresses from Unstructured Text Using Bi-directional Recurrent Neural Networks

Addresses can be classified as unstructured text because they lack meta-information to be directly indexed in databases. Still they demonstrate an internal structure which can used to automatically extract them using machine learning techniques. In this work we describe a machine learning approach to identify addresses in unstructured text (like blogs) using Bidirectional Recurrent Neural Networks (BRNNs). We overcome the problem of lack of training data by generating synthetic free text entries and come up with problem specific features. Our system does not impose any strict condition on the structure or style of addresses leading to many applications in real life.
Reliability of Passive Printed Dipole Antennas Under Extreme Environments

We report flexible dipole antenna designs fabricated using screen printing with a stretchable and conductive CI-1036 silver ink on PDMS that operates at 4.9 GHz. To study the impacts of the surface roughness and skin effect on radio frequency antenna performance, the RMS surface roughness of the straight and serpentine dipole patterns were measured using optical profilometry to be 1.298 and 1.141 µm, respectively. During temperature testing, the serpentine dipole antenna displayed a lower shift in resonant frequency than the straight configuration of 31.4 % for high temperature (80°C > T > 110°C) and 60.2 % for low temperature (−71°C < T < −31°C). COMSOL Multiphysics was used to simulate the effects of thermal expansion on both antenna designs, the serpentine geometry was found to be more resistant to temperature change than the straight dipole.
Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems With State Constraints

In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov’s stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.
Hierarchical RNNs for Waveform-Level Speech Synthesis

Speech synthesis technology has a wide range of applications such as voice assistants. In recent years waveform-level synthesis systems have achieved state-of-the-art performance, as they overcome the limitations of vocoder-based synthesis systems. A range of waveform-level synthesis systems have been proposed; this paper investigates the performance of hierarchical Recurrent Neural Networks (RNNs) for speech synthesis. First, the form of network conditioning is discussed, comparing linguistic features and vocoder features from a vocoder-based synthesis system. It is found that compared with linguistic features, conditioning on vocoder features requires less data and modeling power, and yields better performance when there is limited data. By conditioning the hierarchical RNN on vocoder features, this paper develops a neural vocoder, which is capable of high quality synthesis when there is sufficient data. Furthermore, this neural vocoder is flexible, as conceptually it can map any sequence of vocoder features to speech, enabling efficient synthesizer porting to a target speaker. Subjective listening tests demonstrate that the neural vocoder outperforms a high quality baseline, and that it can change its voice to a very different speaker, given less than 15 minutes of data for fine tuning.
A Critical Appraisal of Contemporary Cyber Security Social Engineering Solutions: Measures, Policies, Tools and Applications

Social engineering is one of the biggest threats organizations face today, as more and more companies are adopting digitalization. This study presents a critical review of the existing measures, tools, and policies for the protection of computer entities, particularly organizations, against cyber security social engineering. These factors were examined through a systematic review of recent studies published on the subject. Our analysis highlights the meaning of social engineering and the problem it poses, and outlines some of the previous efforts that necessitate an inquiry on the subject. There is an urgent need to provide training for employees of such digitalized companies to ensure they understand the risks and ways to avoid them. As socially engineered attacks depend on the human factor, employees’ awareness is the best practice that can be implemented to contain its threats. Other measures include training of non-technical members, awareness programs, new security network, software usage, and security protocols to tackle social engineering threats.
Evaluating deep semantic segmentation networks for object detection in maritime surveillance

Maritime surveillance is important for applications in safety and security, but the visual detection of objects in maritime scenes remains challenging due to the diverse and unconstrained nature of such environments, and the need to operate in near real-time. Recent work on deep neural networks for semantic segmentation has achieved good performance in the road/urban scene parsing task. Driven by the potential application in autonomous vehicle navigation, many of the architectures are designed to be fast and lightweight. In this paper, we evaluate semantic segmentation networks in the context of an object detection system for maritime surveillance. Using data from the ADE20k scene parsing dataset, we train a selection of recent semantic segmentation network architectures to compare their performance on a number of publicly available maritime surveillance datasets.
Scalp Pruritus: Review of the Pathogenesis, Diagnosis, and Management

Scalp pruritus is a frequent problem encountered in dermatological practice. This disorder is caused by various underlying diseases and is a diagnostic and therapeutic challenge. Scalp pruritus may be localized to the scalp or extended to other body areas. It is sometimes not only associated with skin diseases or specific skin changes, but also associated with lesions secondary to rubbing or scratching. Moreover, scalp pruritus may be difficult to diagnose and manage and may have a great impact on the quality of life of patients. It can be classified as dermatologic, neuropathic, systemic, and psychogenic scalp pruritus based on the potential underlying disease. A thorough evaluation of patients presenting with scalp pruritus is important. Taking history and performing physical examination and further investigations are essential for diagnosis. Therapeutic strategy comprises removal of the aggravating factors and appropriate treatment of the underlying condition. All treatments should be performed considering an individual approach. This review article focuses on the understanding of the pathophysiology and the diagnostic and therapeutic management of scalp pruritus.
Extending IOU Based Multi-Object Tracking by Visual Information

Today’s multi-object tracking approaches benefit greatly from nearly perfect object detections when following the popular tracking-by-detection scheme. This allows for extremely simple but accurate tracking methods which completely rely on the input detections as the high-speed IOU tracker. For real world applications, few missing detections cause a high number of ID switches and fragmentations which degrades the quality of the tracks significantly. We show that this problem can be efficiently overcome if the tracker falls back to visual single-object tracking in cases where no object detection is available. In several experiments we show for different visual trackers that the number of ID switches and fragmentations can be reduced by a large amount while maintaining high tracking speeds and outperforming the state-of-the art for the UA-DETRAC and VisDrone datasets.
SERO: A Model-Driven Seamless Roaming Framework for Wireless Mesh Network With Multipath TCP

While modern wireless devices are capable of using multiple WiFi interfaces, the Multipath TCP (MPTCP) protocol has been employed to make full use of the capacity of many radios by enabling multiple path communication simultaneously. To provide exceptional mobility support in wireless networks, a key question is to determine the best handoff strategy to switch between access points or among WiFi/3G interfaces during roaming. In this paper, we propose SERO, a novel model-driven SEamless ROaming framework to optimize layer-2 handoff and vertical handoff for multihomed devices using MPTCP. The proposed framework adopts a measurement-based method to derive the TCP throughput model for wireless communication during handoff. Based on the throughput model, we propose a hybrid handoff strategy that uses multiple WiFi interfaces for data transmission and employs 3G augmentation to bridge the network interruption caused by handoff and to guarantee the total throughput above a predefined threshold for roaming devices. We implement the SERO framework in a real-deployed wireless mesh network testbed, and evaluate its performance by extensive experiments, which shows that SERO achieves performance gain of 26%–180% compared with several existing handoff strategies.
A Study on Artificial-Natural Snow Accretion and Flashover Characteristics of 110kV Post Insulators

Recently a lot of researches have researched the flashover performance of iced insulators but the flashover accidents occur frequently on snow-covered post insulators. At the same time the difference between artificial and natural snow accretion on post insulators has attracted more and more attention. Therefore, the artificial snow-covered method was used in the large scale artificial climate laboratory in this paper, combined with natural snow-covered observation at the natural icing station. In the perspective of morphology, density and conductivity, the snow characteristics between artificial and natural condition were studied. The influence of the snow test parameters on the flashover voltage of the insulators in the substation was studied. Results revealed with the increase of the snowy thickness, the flashover voltage of the post insulator decreased. The snow flashover voltage of the polluted post insulator that was covered uniformly by snow was higher than it that was covered non-uniformly by snow. The pre-stained salt density had the negative influence on flashover voltage.
Wide-field Optical Spectroscopy of Abell 133: A Search for Filaments Reported in X-Ray Observations

Filaments of the cosmic web have long been associated with the threadlike structures seen in galaxy redshift surveys. However, despite their baryon content being dominated by hot gas, these filaments have been an elusive target for X-ray observations. Recently, detections of filaments in very deep (2.4 Ms) observations with Chandra were reported around Abell 133 (z = 0.0559). To verify these claims, we conducted a multiobject spectrographic campaign on the Baade 6.5 m telescope around Abell 133; this resulted in a catalog of ∼3000 new redshift measurements, of which 254 are of galaxies near the cluster. We investigate the kinematic state of Abell 133 and identify the physical locations of filamentary structure in the galaxy distribution. Contrary to previous studies, we see no evidence that Abell 133 is dynamically disturbed; we reject the hypothesis that there is a kinematically distinct subgroup (p = 0.28) and find no velocity offset between the central galaxy and the cluster ( ). The spatial distribution of galaxies traces the X-ray filaments, as confirmed by angular cross-correlation with a significance of ∼5σ. A similar agreement is found in the angular density distribution, where two X-ray structures have corresponding galaxy enhancements. We also identify filaments in the large-scale structure of galaxies; these filaments approach the cluster from the direction the X-ray structures are seen. While more members between R200 and 2 × R200 are required to clarify which large-scale filaments connect to the X-ray gas, we argue that this is compelling evidence that the X-ray emission is indeed associated with cosmic filaments.
Performance of a powerful error-correcting and -detecting coding scheme for the North American Basic Teletext System (NABTS) for random independent errors: methods, equations, calculations and results

For a powerful layered, upward- and downward-compatible error-correcting and error-detecting scheme for NABTS, various bit error rate (BER) related performance measures are derived and calculated for random independent errors. The methods, equations, calculations and results are given for the least powerful one-byte suffix codes, for the two-byte suffix code, called code C, and for the double and single bundle codes formed by using code C for each data block (i.e. horizontally) and also vertically, thus forming a product code, for a specified, but variable, number of data blocks. Performance bounds and equations for probabilities of correct decoding of error and of decoding failure are given. The weight enumerators for a number of one-byte suffix codes are calculated, and those of weight four are classified into types depending on the number of ones occurring in a byte, and in other arrangements. Performance analyses and comparisons with a code for Japanese teletext are included. Analyses used in computer simulation studies are described. >
Human Support Agent for Design and Diagnosis using Classifier System

As is well known, an advanced knowledge and know-how are needed in the design and the diagnosis work. Further, human experts can cope with the recent trend of customers needs. Therefore, the design and the diagnosis work have been privately performed in the past, and its information cannot be shared. In addition, the number of experts is decreasing. It is a very important problem to maintain and to extend experts technologies having been built up. For the purpose, methods and systems for technical inheritance of the advanced techniques of the skilled engineers are needed to train the unskilled operators and also can assist skilled operators. In this research, attention is focused on the design of analog filter circuit. To attain the target, the circuit structural classifier system to support design work is developed. Furthermore, simulation technology for hot strip rolling mills based on distributed agents is presented aimed to develop tools for the diagnosis of hot strip rolling mills operation.
Text-to-speech algorithms based on FFT synthesis

The authors present FFT synthesis algorithms for a French text-to-speech system based on diphone concatenation. FFT synthesis techniques are capable of producing high quality prosodic modifications of natural speech. Several approaches are presented to reduce the distortions due to diphone concatenation. They are based on appropriate manipulations of the phase spectrum, either by phase equalization across all the diphones, or by phase smoothing between successive diphones. The resulting speech is significantly better quality than with conventional LPC synthesis. An experiment to reduce the computational cost by performing all the FFTs off-line is described. The resulting speech is slightly degraded with respect to 'full' FFT synthesized speech, but it remains more natural in comparison with the LPC speech. <<ETX>>
Microsoft's Management Reporting: SAP, Data Warehousing, and Reporting Tools

INTRODUCTION In the early 1990s, Microsoft’s financial reports were generated by a proprietary general ledger program running on a mainframe. Financial information was provided to users in the form of preformatted paper reports (e.g., business unit profit and loss statements and other consolidated financial statements such as those found in a company’s annual report). The first close of the year took three weeks and subsequent month-end closes took two weeks each. At least half of this time could be attributed to the time-consuming processes of printing, copying, faxing, and mailing paper statements—embarrassing in an information business that prides itself on developing and using technology effectively. Unfortunately, Microsoft’s legacy financial systems permitted no improvement in the monthly closing cycle. The Finance Group wanted to fulfill Bill Gates’ vision of putting information at people’s fingertips and getting out of the paper-handling business. To do this, they implemented SAP, loaded financial data into a data warehouse, developed user-friendly reporting tools, and they made the data, the reporting tools, and some preformatted financial reports accessible to authorized users via the company’s intranet. These technologies have had a substantial impact: the month-end close has now been reduced to four days.