Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Representative Subset Selection over Sliding Windows

Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. Existing literature models RSS as the submodular maximization problem to capture the “diminishing returns” property of the representativeness of selected subsets, but often only has a single constraint (e.g., cardinality), which limits its applications in many real-world problems. To capture the data recency issue and support different types of constraints, we formulate dynamic RSS in data streams as maximizing submodular functions subject to general $d$d-knapsack constraints (SMDK) over sliding windows. We propose a KnapWindow framework (KW) for SMDK. KW utilizes the KnapStream algorithm (KS) for SMDK in append-only streams as a subroutine. It maintains a sequence of checkpoints and KS instances over the sliding window. Theoretically, KW is $\frac{1-\varepsilon }{1+d}$1-ɛ1+d-approximate for SMDK. Furthermore, we propose a KnapWindowPlus framework (KW$^{+}$+) to improve upon KW. KW$^{+}$+ builds an index SubKnapChk to manage the checkpoints and KS instances. SubKnapChk deletes a checkpoint whenever it can be approximated by its successors. By keeping much fewer checkpoints, KW$^{+}$+ achieves higher efficiency than KW while still guaranteeing a $\frac{1-\varepsilon ^{\prime }}{2+2d}$1-ɛ'2+2d-approximate solution for SMDK. Finally, we evaluate the efficiency and solution quality of KW and KW$^{+}$+ in real-world datasets. The experimental results demonstrate that KW achieves more than two orders of magnitude speedups over the batch baseline and preserves high-quality solutions for SMDK over sliding windows. KW$^{+}$+ further runs 5-10 times faster than KW while providing solutions with equivalent or even better utilities.
Maximizing Multifaceted Network Influence

An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is to assign k distinct pieces of an information campaign OIPA to k promoters, so as to achieve the highest viral adoption in a network. We express adoption by users with a logistic model, and show that approximating OIPA within any constant factor is NP-hard. Even so, we propose a branch-and-bound framework for problem with an (1-1/e) approximation ratio. We further optimize this framework with a pruning-intensive progressive upper-bound estimation approach, yielding a (1-1/e-\varepsilon) approximation ratio and significantly lower time complexity, as it relies on the power-law properties of real-world social networks to run efficiently. Our extensive experiments on several real-world datasets show that the proposed approaches consistently outperform intuitive baselines, adopted from state-of-the-art IM algorithms. Furthermore, the progressive approach demonstrates superior efficiency with an up to 24-fold speedup over the plain branch-and-bound approach.
Analysis of Influence of Frequency Converters with Active Rectifiers on the Power Quality in Internal Power Supply Systems of Industrial Enterprises

This paper studies power factors in the internal power supply system of metallurgical plant frequency converters with active rectifiers. A simulation model of the three-level twelve-pulse active rectifiers has been developed and implemented in the Matlab/Simulink environment for investigation purposes. Four different pulse-width modulation algorithms used at the operated active rectifiers and the DC-link voltage control system have been realized. In addition, the harmonic composition of consumed current and network voltage at the active rectifier connection point have been analyzed. The compliance of the harmonics' level with the power quality standard (GOST 32144–2013) has been verified. The conclusions about the efficiency of existing pulse-width modulation algorithms have been made.
Transthyretin Glu54Leu – an unknown mutation within the Swedish population associated with amyloid cardiomyopathy and a unique fibril type

Abstract For the first time, we report of a Swedish family of five individuals with a TTR Glu54Leu (p. Glu74Leu) mutation in the transthyretin gene. This mutation has been previously described a few times in the literature, but no phenotypic or clinical description has been done before. The most common mutation in the Swedish population is TTRVal30Met and is mostly found in the Northern part of Sweden. Interestingly, the TTRGlu54Leu mutation was found in the same endemic area. The main phenotype of the TTR Glu54Leu patients is severe cardiomyopathy, which resulted in heart transplantation for the index person. As previously seen for ATTR amyloidosis patients with mainly cardiomyopathy, the amyloid fibrils consisted of a mixture of full-length and fragmented TTR species. However, western blot analyses detected a previously unrecognized band, indicating that these patients may have a third, so far unrecognized, fibril composition type that is distinct from the usual type A band pattern.
A Novel Input Set for LSTM-Based Transport Mode Detection

The capability of mobile phones are increasing with the development of hardware and software technology. Especially sensors on smartphones enable to collect environmental and personal information. Thus, with the help of smartphones, human activity recognition and transport mode detection (TMD) become the main research areas in the last decade. This study aims to introduce a novel input set for daily activities mainly for transportation modes in order to increase the detection rate. In this study, the frame-based novel input set consisting of time-domain and frequency-domain features is fed to LSTM network. Thus, the classification ratio on HTC public dataset for 10 different transportation modes is climbed up to 97% which is 2% more than the state-of-the-art method in the literature.
LDC: A Lower-Level Driven Compaction Method to Optimize SSD-Oriented Key-Value Stores

Log-structured merge (LSM) tree key-value (KV) stores have been widely deployed in many NoSQL and SQL systems, serving online big data applications such as social networking, bioinfomatics, graph processing, machine learning, etc. The batch processing of sorted data merging (i.e., compaction) in LSM-tree KV stores greatly improves the efficiency of writing, leading to good write performance and high space efficiency. Recently, some lazy compaction methods were proposed to further promote the system throughput through delaying the compaction to accumulate more data within a compaction batch. However, the batched writing manner also leads to significant tail latency, which is unacceptable for online processing, and the newly proposed lazy approaches worsen the tail latency problem. Furthermore, the unbalanced read/write performance of the widely deployed SSDs make the performance optimization harder. Aiming to optimize both the tail latency and the system throughput, in this paper, we propose a novel Lower-level Driven Compaction (LDC) method for LSM-tree KV stores. LDC breaks the limitations of the traditional upper-level driven compaction manner and triggers practical compaction actions by lower-level data. It has the benefits of both decreasing the compaction granularity effectively for smaller tail latency and reducing the write amplification of LSM-tree compaction for higher throughput. We have implemented LDC in LevelDB; the experimental results indicate that LDC can reduce the 99.9th percentile latency for 2.62 times compared with the traditional upper-level driven compaction mechanism, and achieve 56.7% ~ 72.3% higher system throughput at the same time.
Performance Assessment of Perturbation and Observation Algorithm for Wind Turbine

The paper describes a study of perturbation and observation (P&O)maximum power point tracking (MPPT)algorithm that can be applied to wind turbine power stations. As the maximum power operating point of wind turbine varies with changing meteorological conditions (e.g. wind speed and temperature), an important issue in the design of efficient wind turbine power station is an ability to track the optimal operation point correctly. In the cases of rapidly changing wind speed, the efficiency of maximum power point tracking algorithms can drop noticeably due to the inertia of turbine. Simulation modeling results with the proposed algorithm implemented in a Matlab/Simulink show that algorithm has successfully tracked the optimal operation point, even in cases of rapidly changing atmospheric conditions.
A Survey on Text Mining Techniques

As there is fast growth in digital data collection techniques it has made way for large amount of data. Greater than 85% of present day data is comprised of unsaturated and unstructured data. Determining the definite patterns and trends to examine a textual data is biggest issue in text mining The various domains associated together in data mining are text mining, web mining, graph mining, and sequencing mining. The selection of proper and correct technique of text mining enhances the hustle and by lowering the period and struggle done to mine important information. Here, we talk about text data mining, various techniques of text data mining and also application of text data mining. Text data mining is used for obtaining stimulating and fascinating designs from the unsaturated texts which are derived from various sources. It changes words, phrases and sentences of an unstructured information into mathematical value linking with the saturated information in the database and analyses it with traditional data mining techniques. Information extraction, information retrieval, summarization, categorization and clustering are the different techniques of text mining.
Vehicle Availability Profiling from Diverse Data Sources

An understanding of the movement and utilisation rates of vehicles has many applications in the ‘smart city’. The increasing availability of location and movement data from smartphones, in-vehicle loggers etc. allows for new applications of vehicle use analytics to be developed using a diverse range of data sources. However, for wide-scale application it may not be feasible to rely on consistent data from all possible sources - a need arises to process disparate data sources into a unified format. Raw user location data also presents significant issues around user privacy and the need to securely store and transmit any personally identifiable data. This paper covers the problem definition and development of a system to classify vehicle user driving patterns. A system was proposed to allow user driving patterns to be characterised in a way that does not explicitly store large volumes of location data while retaining key information needed for behavioural analysis. User driving data was converted to a personal profile based on statistical likelihood of vehicle use over a 24-hour period. Dynamic Time Warping was used to quantify the match between a new user's calculated profile and established driving archetypes. Additional profile features were tested in trained multi-class classification models including typical journey length, no. journeys etc. This was found to reduce the number of days of data needed to make a match for most users. This increases the feasibility of representing vehicle users in relation to driving archetypes rather than explicitly storing sensitive location data.
A Hybrid OSD-SC Decoding Algorithm for Polar Codes

In this paper, we propose a low complexity hybrid ordered statistics decoding (OSD) successive cancellation (SC) decoding algorithm for the polar codes. By replacing smaller size SC decoder with OSD decoder, we can improve the error correcting performance of the SC algorithm. The size of the OSD decoder can be changed to control the trade-off between the decoding complexity and the error correcting performance of the proposed algorithm. We also provide a method to further simplify the proposed algorithm. Simulation results show that the proposed algorithm offers about 0.3 dB gain comparing to the SC at target bit error rate (BER) 10$^{-5}$ with code length N = 1024, 512,256 and code rate R = 1 /3. The impact of the OSD size is also simulated and analyzed.
Enhanced DC and RF performance of AlGaN/GaN HEMTs on CVD-Diamond in high power CW operation

We have investigated the DC and RF performances of AlGaN/GaN HEMTs on CVD-Diamond at high power density continuous wave (CW) operating condition. About 4-times improved DC and RF performances were observed in GaN/Dia HEMTs, which was due to the lower rise in junction temperature at higher CW operating power density. This was confirmed by theoretical TCAD simulation. The improved DC and RF performance from GaN/Dia HEMTs are promising for high power density CW operating DC and RF applications.
Detecting Buffer-Overflow Vulnerabilities in Smart Grid Devices via Automatic Static Analysis

As a modern power transmission network, smart grid connects plenty of terminal devices. However, along with the growth of devices are the security threats. Different from the previous separated environment, an adversary nowadays can destroy the power system by attacking these devices. Therefore, it’s critical to ensure the security and safety of terminal devices. To achieve this goal, detecting the pre-existing vulnerabilities of the device program and enhance the terminal security, are of great importance and necessity. In this paper, we propose a novel approach that detects existing buffer-overflow vulnerabilities of terminal devices via automatic static analysis (ASA). We utilize the static analysis to extract the device program information and build corresponding program models. By further matching the generated program model with pre-defined vulnerability patterns, we achieve vulnerability detection and error reporting. The evaluation results demonstrate that our method can effectively detect buffer-overflow vulnerabilities of smart terminals with a high accuracy and a low false positive rate.
Secret Information Transmission Model based on Non-complete Orthogonal Basis

The secret information transmission model is studied and proved based on orthogonal basis space. In order to maintain the main information of the secret information’s carrier, which is usually media form, paper gets the principal component of media information by Principal Component Analysis (PCA) as the orthogonal basis. Due to the reasonable compression of the carrier by PCA, paper constructs two non-complete orthogonal basis spaces based on the principal component to reduce construction’s difficulty and complexity. The experiment verified the efficiency and feasibility about the model. And the form of the secret information is flexible. The capacity of the secret information is more. So it has Obvious advantages.
Detection of Vowel-Like Speech Using Variance of Sample Magnitudes

Vowel, semi vowel and diphthong sound units are collectively referred to as vowel-like speech (VLS). VLS are dominant voiced regions in a given speech signal. Consequently, within a short-analysis frame the variance of sample magnitudes (VSM) is significantly higher for VLS when compared with other speech regions. In this work, a signal processing approach is proposed to robustly extract the VSM within an analysis frame. The VSM at each time instant is then non-linearly mapped (NLM) using negative exponential function to suppress the fluctuations. The NLM-VSM values are nearly constant and significantly less in magnitude for VLS than other speech, silence and noise regions. The NLM-VSM is used as a front-end feature for detecting the VLS in a given speech signal. The experimental results presented in this paper show that, for clean as well as noisy speech signals, the proposed feature outperforms some of the earlier reported features for the task of detecting VLS and corresponding onset and offset points.
A Review on Self Destructing Data:Solution for Privacy Risks in OSNs

Online Social Networks(OSN) plays a vital role in our day to day life. The most popular social network, Facebook alone counts currently 2.23 billion users worldwide. Online social network users are aware of the various security risks that exist in this scenario including privacy violations and they are utilizing the privacy settings provided by OSN providers to make their data safe. But most of them are unaware of the risk which exists after deletion of their data which is not really getting deleted from the OSN server. Self destruction of data is one of the prime recommended methods to achieve assured deletion of data. Numerous techniques have been developed for self destruction of data and this paper discusses and evaluates these techniques along with the various privacy risks faced by an OSN user in this web centered world.
Research of Opportunities for Reducing Transmission Errors in Orthogonal Frequency-Division Multiplexing Signals

The paper discusses the features of the transfer of orthogonal frequency-division multiplexing signals. The main advantages of using the signals if we compare it to a single-carrier scheme are their ability to withstand the difficult conditions in the channel. The block wiring diagram for receiving modems with a module of multiple averaging is shown. The comparison of using Single Input Single Output, Single Input Multiple Output (coherent averaging) and Single Input Multiple Output (the proposed method) is carried out. The graphical dependence are constructed and it is shown that the proposed approach, the number of errors can be reduced by several times.
Operational Profile and Software Testing: Aligning User Interest and Test Strategy

Context: The software's operational profile is a representation of how users use the software, in which the most commonly-used parts of the software are identified. There is evidence of a possible mismatch between the tested parts of the software and the operational profile of the software. Objective: This paper aims to present a doctoral research that investigates the use of software operational profile as a resource to improve software quality from the point of view of users. As one of the expected contributions, the research proposes a strategy in which the operational profile of the software is used to: a) evaluate and adapt a test suite based on the operational profile of the software; b) as a prioritization criterion that allows, given a set of defects, to identify the defects that have the greatest impact on the operation of the software by the users and, thus, to consider this impact in the pricing of the defects. Method: To present the research, this paper describes: a) the problem addressed and the purpose of the research; b) the results obtained by the activities carried out to evaluate the feasibility of the research (systematic mapping of the literature, systematic review of the literature and exploratory study); c) the hypotheses to be investigated; d) expected contributions; e) the current state of the research. Results: The results obtained by the activities carried out were favorable to the accomplishment and continuity of the research, evidencing the originality of the research and the proposed strategy. Conclusion: Once the feasibility of the research and the progress that has been obtained in its execution are verified, the results to be obtained are promising and, consequently, the expected contributions must be obtained.
Identification of Early Vascular Dementia Patients With EEG Signal

Effort has been made to find biomarkers for vascular dementia (VaD). Nevertheless, the current findings are typically obtained through statistical tests of group level differences. In clinical practice, however, it is more common to perform individual level inferences, e.g., to determine if a subject is suffering from VaD, which cannot be resolved with statistical analysis. The goal of this study is to develop a method to effectively discriminate early VaD patients from normal controls by combining EEG features with machine learning methods. The EEG signals were recorded from a total of 15 VaD patients and 21 controls during a visual oddball task. Interregional directed connectivity was derived from directed transfer function (DTF) analysis and used as features in classification. Three machine learning methods, including linear discriminant analyais (LDA), error back-propagation (BP) neural network, and support vector machine (SVM) were used as classifiers, and their classification performance was compared. It was found that VaD patients can be effectively identified using the BP and SVM classifiers with high accuracy. In particular, when the SVM classifier was combined with feature selection by Fisher score, it reached an accuracy 86.11%, sensitivity 86.67%, and specificity 85.71%. The area under the curve (AUC, 0.854) indicates a good identification of VaD patients from the normal controls. Since the EEG is noninvasive, inexpensive, and widely available to use, the current study presents a novel clinical application of machine learning methods and could facilitate automatic screening and diagnosis of the VaD at an early stage in future.
A weighted optimization for Fourier Ptychographic Microscopy

Fourier ptychography can be implemented as a phase retrieval optimization algorithm that iteratively solves for high resolution spectrum from low resolution images. In prior art, all the low resolution images were considered equally in the optimization. In this paper, we propose a weighted optimization algorithm to enhance the quality of reconstruction with the same convergence speed. Our method is motivated by the observation that bright field and dark field low resolution images have significantly different pixel intensities. Therefore, we weight their estimated error differently in the optimization. Though the proposed method is both conceptually and computationally simple, it dramatically improves the quality of reconstruction. We also show that the weighted optimization algorithm converges to a lower mean squared error value compared to the conventional optimization. We validate our approach on several low resolution images from an experimental dataset.
Analyzing the Music Perception Based on Physiological Signals

Music has a profound influence on physiological and psychological effects on humans. This research aimed to investigate emotional responses to two types of Indian classical music using their effective changes in physiological parameters (heart rate and respiratory rate) along with behavioral responses. Thirty two healthy non- musician participated in this study, where 17 participants’ listened to happy music and the remaining 15 participants’ listened to sad music. ECG and Respiration were recorded for seven minutes and behavioral data were obtained. There was significant increase in heart rate and respiratory rate while listening to happy music (p<0.05) and a significant increase in heart rate were also noted while listening to sad music (p<0.05) but no changes were observed in the respiratory rate. The valence was found to have a pivotal effect on heart rate whereas the arousal was found to have a crucial effect on respiratory rate.