Miracle-dz/UniFAR-FTUs huggingface dataset:
Summarizing Provenance of Aggregate Query Results in Relational Databases

Data provenance is any information about the origin of a piece of data and the process that led to its creation. Most database provenance work has focused on creating models and semantics to query and generate this information. While comprehensive, provenance information remains large and overwhelming, which can make it hard for provenance systems to support data exploration. We present a new approach to provenance exploration that builds on data summarization techniques. We contribute two novel summarization schemes for the provenance of aggregation queries: Impact summaries, and comparative summaries. We show with experiments that our techniques incur little overhead compared to basic summaries. We conduct a survey to show that our approaches are useful to users.
On the Effectiveness of Fekete’s Lemma in Information Theory

Fekete’s lemma is a well known assertion that states the existence of limit values of superadditive sequences. In information theory, superadditivity of rate functions occurs in a variety of channel models, making Fekete’s lemma essential to the corresponding capacity problems. We analyze Fekete’s lemma with respect to effective convergence and computability and show that Fekete’s lemma exhibits no constructive derivation. In particular, we devise a superadditive, computable sequence of rational numbers so that the associated limit value in the sense of Fekete’s lemma is not a computable number. We further characterize the requirements for effective convergence and investigate the speed of convergence, as proposed by Rudolf Ahlswede in his 2006 Shannon lecture.
Smart and Context-Aware System employing Emotions Recognition

People have the ability to make sensible assumptions about other people’s emotional states by being sympathetic, and because of our common sense of knowledge and the ability to think visually. Over the years, much research has been done on providing machines with the ability to detect human emotions and to develop automated emotional intelligence systems. The computer’s ability to detect human emotions is gaining popularity in creating sensitive systems such as learning environments, health care systems and real-world. Improving people’s health has been the subject of much research. This paper describes the formation as conceptual evidence of emotional acquisition and control in intelligent health settings. The authors of this paper aim for an unconventional approach with a friendly look to get emotional scenarios from the system to establish a functional, non-intrusive and emotionally-sensitive environment where users can do their normal activities naturally and see the program only when pleasant mood activating services are received. The context-sensitive system interacts with users to detect and differentiate emotions through facial expressions or speech recognition, to make music recommendations and mood color treatments with the services installed on their IoT devices.
A Single Stage Three Phase Modified Vienna Rectifier with Power Electronic Scott Connection Transformer

The need for non regenerative pulse width modulated type rectifier is increasing day by day in areas requiring unidirectional power flow. A three phase modified Vienna Rectifier based on power electronic Scott connection transformer with reduced switch count is proposed in this paper. Power electronic Scott connection transformer avail the advantage of size and weight reduction. Major advantages are: there is a single stage conversion, there is no need of intermediate bulky dc link capacitor which is not reliable, also high power density is achieved. This paper uses decoupled d-q reference frame control technique for maintaining unity power factor and to give rectified output.
DeBinelle: Semantic Patches for Coupled Database-Application Evolution

Databases are at the core of virtually any software product. Changes to database schemas cannot be made in isolation, as they are intricately coupled with application code. Such couplings enforce collateral evolution, which is a recognised, important research problem. In this demonstration, we show a new dimension to this problem, in software that supports alternative database backends: vendor-specific SQL dialects necessitate a simultaneous evolution of both, database schema and program code, for all supported DB variants. These near-same changes impose substantial manual effort for software developers. We introduce DeBinelle, a novel framework and domain-specific language for semantic patches that abstracts DB-variant schema changes and coupled program code into a single, unified representation. DeBinelle further offers a novel alternative to manually evolving coupled schemas and code. DeBinelle considerably extends established, seminal results in software engineering research, supporting several programming languages, and the many dialects of SQL. It effectively eliminates the need to perform vendor-specific changes, replacing them with intuitive semantic patches. Our demo of DeBinelle is based on real-world use cases from reference systems for schema evolution.
Understanding volumetric capacitance in conducting polymers

© 2016 Wiley Periodicals, Inc. Recent measurements in poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) films show that capacitance scales with film volume. We discuss the ramifications of this finding and propose a simple model that describes capacitance in terms of sites in which ions injected from the electrolyte replace holes that are extracted from the film by a metal contact. We propose that volumetric capacitance is inversely proportional to the average distance between these sites. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 1433–1436.
Muscle Artifact Removal Toward Mobile SSVEP-Based BCI: A Comparative Study

Steady-state visual evoked potential (SSVEP) serves as one of the extensively utilized paradigms for brain–computer interface (BCI). SSVEP-based BCI has advantages of good classification accuracy, high information transfer rate (ITR), and little user training. However, there exist few studies investigating SSVEP-based BCI in the mobile situation, where electroencephalography (EEG) data are inevitably contaminated by muscle activities, and meanwhile, a limited number of channels are preferred. In this study, two major muscle artifact removal schemes are investigated in the dynamic scenario. One scheme adopts state-of-the-art blind source separation (BSS) methods without electromyogram (EMG) reference, while the other utilizes auxiliary EMG channels as a reference in the denoising process. To evaluate the performance of two schemes, we have tested them on real EEG data collected from ten subjects. The results demonstrate that, in the few-channel condition of SSVEP data, there are classification accuracy improvements of 9.93% for the canonical correlation analysis (CCA) indicator and 15.72% for the task-related component analysis (TRCA) indicator after processed by recursive least-squares (RLS) filtering with auxiliary EMG, and in the single-channel condition, there are classification accuracy improvements of 22.00% for the CCA indicator and 24.42% for the TRCA indicator after RLS filtering, averaged across all the subjects and data lengths. However, the BSS-based methods fail to improve the classification accuracies of SSVEP. Thus, it is of necessity to mount additional EMG channels for effective muscle activity suppression. This study provides an insightful suggestion for future real-life BCI applications.
Relational Header Discovery using Similarity Search in a Table Corpus

Column headers are among the most relevant types of meta-data for relational tables, because they provide meaning and context in which the data is to be interpreted. Headers play an important role in many data integration, exploration, and cleaning scenarios, such as schema matching, knowledge base augmentation, and similarity search. Unfortunately, in many cases column headers are missing, because they were never defined properly, are meaningless, or have been lost during data extraction, transmission, or storage. For example, around one third of the tables on the Web have missing headers. Missing headers leave abundant tabular data shrouded and inaccessible to many data-driven applications.We introduce a fully automated, multi-phase system that discovers table column headers for cases where headers are missing, meaningless, or unrepresentative for the column values. It leverages existing table headers from web tables to suggest human-understandable, representative, and consistent headers for any target table. We evaluate our system on tables extracted from Wikipedia. Overall, 60% of the automatically discovered table headers are exact and complete. Considering more header candidates, top-5 for example, increases this percentage to 72%.
Robust Curb Detection Based on the Accessible Route Analysis and Key Frames Prediction

For autonomous driving in urban environments, road curb plays a significant role in tasks such as lane-keeping, assisted localization, and path planning. A real-time robust curb detection algorithm based on 3D LiDAR is proposed in this paper. Firstly, the iterative beam model is applied to get the accessible route of the road to obtain the starting point of the search step for each scan line. Secondly, the candidate curb points are extracted according to the spatial distribution characteristics of the point cloud. To effectively combine the historical boundaries information, a Bayesian filter is used to track the road width to reduce the false detection of curb points when the boundaries are interrupted, or on-road obstacles appear. The proposed algorithm is tested in different road environments. The experimental results show that our method has strong scene adaptability. The detection accuracy is over 90%, and the average runtime is 34.62 ms.
Efficient 2-Hop Labeling Maintenance in Dynamic Small-World Networks

Shortest path computation is a fundamental operation in small-world networks and index-based methods, especially 2-hop labeling, are commonly applied which have achieved high query efficiency. However, small-world networks keep evolving in real life, making it indispensable to study the maintenance of shortest path index. In this work, we adopt the state-of-the-art Parallel Shortest-distance Labeling (PSL) as the underlying 2-hop labeling construction method, and design algorithms to support efficient update of the index given edge weight change (increase and decrease) in the network. Specifically, we focus on weighted PSL (WPSL) and propose the update propagation mechanism for both synchronous propagation and asynchronous propagation. We then identify the curse of pruning power generated for the propagation under edge weight increase, and solve this problem with a balance between index size and effectiveness. Finally, we extend the proposed asynchronous propagation method to Pruned Landmark Labeling (PLL) for faster index maintenance and query processing with smaller index size. Our experimental results on real-life and synthetic networks demonstrate the superiority of our algorithms on index maintenance.
Comparison of the Stewart Platform Linear Actuator Variants Using Simulation Tools

The paper deals with the use of the Stewart platform simulation model created in the Matlab/Simulink and the Simscape library, for comparison purposes of used linear actuators characteristics. There are introduced linear electric and hydraulic actuators, its strengths and weaknesses and its suitability for the Stewart platform application. In the next part, there are presented predefined simulation models of both actuators and tools that allow to switch easily between hydraulic and electric variant of the Stewart platform simulation model. In the last part, the obtained simulation results are compared. The gained insight can be valuable for choosing a suitable variant for the Stewart platform application.
A class of Generic Approximate Sparse Pseudoinverse Matrix Techniques based on incomplete QR factorization

A class of Generic Approximate Sparse Pseudoin-verse matrices based on modified incomplete QR factorizations with Givens rotations is presented. The filtering criteria for the Incomplete QR factorization have been modified to improve the quality of the incomplete upper triangular factor R, by removing the restriction on the number of nonzero elements per row. Moreover, the Givens rotation matrices are dropped during the incomplete factorization process in order to reduce memory requirements. The proposed approximate pseudoinverse preconditioning scheme is computed, using a "restricted" solution process, based on approximate pseudoinverse sparsity patterns, derived from powers of the sparsified incomplete upper triangular matrix R. The proposed preconditioning scheme is used in conjunction with the Explicit Preconditioned Conjugate Gradient Least Squares method. Numerical results indicating the applicability and efficiency of the proposed algorithmic schemes for solving various model problems are given.
A Data Mining based Optimization of Selecting Learning Material in an Intelligent Tutoring System for Advancing STEM Education

Subsequent to the data deluge of the internet era and the recent advancement in big data technologies, it is easy to affirm the continuous application of such technological innovation to tackling a wide array of students' educational needs. The field of artificial intelligence and machine learning have improved education learning outcomes. However, the problem of generalized traditional supportive collaboration scripts for all students irrespective of the student's learning traits and position on the learning spectrum leads to less than optimum result in their educational pursuits. This paper presents a novel approach that uses data mining algorithm to optimize the selection of educational resources for students based on their learning traits and the six factors that cofound instructional content and delivery with a focus on students with learning disabilities for STEM subjects.
MNSIM-TIME: Performance Modeling Framework for Training-In-Memory Architectures

Emerging memristors and Processing-In-Memory (PIM) architectures have shown powerful capabilities in improving the computing energy efficiency of neural network (NN) algorithms. Existing work has proposed the memristor-based NN training architecture [2], which can improve more than 10x energy efficiency improvement compared with CMOS-based solutions. In this paper, we propose a behavior-level modeling framework for memristor-based training-in-memory architectures, called MNSIM-TIME. Compared with existing modeling tools, MNSIMTIME supports configurable architecture design and fast hardware performance modeling, which helps researchers to realize efficient design space exploration in the early architecture design stage.
A Single-Letter Upper Bound on the Mismatch Capacity via a Multicasting Approach

We introduce a new analysis technique that we use to derive a single-letter upper bound on the mismatch capacity of a stationary point-to-point memoryless channel with decoding metric q. Our bound is obtained by considering a multicast transmission over a two-user broadcast channel with decoding metrics q and ρ at the receivers, referred to as (q, ρ)-surely degraded. This channel has the property that the intersection event of the correct q-decoding of receiver 1 and the erroneous ρ-decoding of receiver 2 has zero probability for any codebook of a certain composition P. Our bound holds in the strong converse sense of exponential decay of the probability of correct decoding at rates above the bound. Several examples that demonstrate the strict improvement of our bound compared to previous results are analyzed. Further, we detect equivalence classes of isomorphic channel-metric pairs (W, q) that share the same mismatch capacity. We prove that if the class contains a matched pair, then our bound is tight and the mismatch capacity of the entire class is fully characterized and is equal to the LM rate, which is the highest rate achievable by random coding, and may be strictly lower than the Shannon (matched) capacity.
A 3D Real Object Recognition and Localization on SLAM based Augmented Reality Environment

this paper introduces a method for recognizing real world objects in AR(Augmented Reality) environment and visualizing virtual information based on the objects. Existing AR shows high dependence on markers. The use range of the virtual space is limited to narrow marker space and the placement and tracking of virtual objects in a 3D is also limited to the space centered on the marker. The method constructs a map of the space in the form of a point cloud using SLAM, and performs real-world object recognition of the constructed wide AR space in real time. As a result, the degree of freedom of space utilization increases, and it is helpful in applying AR such as information display of real-world objects by accurately recognizing and localizing the location of objects existing in the real-world space.
An Event-Driven Spectrum-Aware Routing Protocol in Cognitive Radio Sensor Networks Using Fuzzy Logic

An event-driven spectrum sensing routing algorithm based on fuzzy logic (FLSAC) is proposed to make better use of spectrum resources and reduce the energy consumption of cognitive radio sensor networks (CRSN). The algorithm aggregates nodes with more common channels into clusters in a self-organizing way. Using fuzzy logic, cluster heads are determined according to node residual energy, node neighborhood degree, and node relay degree, which reduces the overhead of cluster head selection and balances the energy consumption between cluster heads. Cluster heads and gateway nodes use multi-hop routing to improve inter-cluster connectivity. Simulation results show that in multi-round event-driven CRSN, FLSAC algorithm outperforms LEUACH and CogLEACH algorithms in energy consumption, network load balancing and, packet transmission rate.
Analytical Modeling and Design of Micro Ring Modulator

Silicon photonics circuits with ring and bus waveguides are of interesting topics. The modelling of the ring modulator is analyzed in this work. The modulators are very important unit of the communication system. Simulating these structures require more time and longer propagation lengths. The 2.5D finite difference time domain also called variational FDTD is used to simulate the ring modulator. Ring modulator modal response is analyzed and the wavelength used is in the Conventional band. The Conventional band wavelength is from 1530 nm to 1565 nm band in optical communication systems. The micro ring modulator structure has effective index of 1.244672 and group index of 1.557. Transfer electric mode is used for the analysis.
Identification of Indian Medicinal Plants from Leaves using Transfer Learning Approach

India is well-known for providing the optimal habitat for a wide variety of medicinal plants. Numerous parts of the plants are utilized as crucial elements for the creation of natural medications. Utilization of image processing and Computer Vision strategies for the distinguishing proof of the medicinal plants are extremely urgent as a large number of these plants are under extinction according to the IUCN (International Union for Conservation of Nature) records. Subsequently, the digitization of helpful therapeutic plants is urgent for the protection of biodiversity. This paper examines Convolutional Neural Network (CNN) based methodologies for distinguishing Indian leaf species. In recent times, numerous Deep Learning structures have been utilized in the distinguishing proof and characterization of a wide assortment of plants. This research chiefly centers around identifying the therapeutic plants that are accessible in rustic territories. To do so, three notable pre-trained CNN architectures namely ResNet101, InceptionV3, and VGG16 that were trained for the ImageNet database were chosen by actualizing the Transfer Learning technique. These models were inspected with their pre-trained weights for the Ayur Bharat dataset that was created utilizing 10 unique classes of medicinal plants that sum up to a total of 10000 images. The performance of these models was improved via the Canny edge detection method and a comparison was carried out between these three architectures that were trained, both without and with, preprocessing techniques. The best classification performance for the Ayur Bharat dataset was attained by the InceptionV3 architecture trained with the Canny edge detection pre-processing technique, with the finest validation accuracy and F1-score of 0.9732 and 0.9653 respectively.
PROLIPOSOME: NOVEL DRUG DELIVERY SYSTEM

Liposome are biologically inert microscopic vesicles in which aqueous compartment is enclosed by a lipid bilayer membrane. Liposomes are most promising and broadly applicable drug delivery system. But liposomes are associated with poor stability problem and this leads to be storage problem . To overcome the stability problem proliposomes were discovered by Payne et al. in 1986.Proliposomes are defined as dry, free flowing particles with a dispersed system that can immediately form a liposomal suspension when in contact with water. Proliposome are helped to improve the bioavailability. This paper reviews the method of preparation, merits, evaluation of Proliposomes and highlights its potential to be exploited for different routes of administration.
Detection of Key Targets in Video Logging Based on Convolutional Neural Network

Video logging is an important means to detect the state of wellbore and ensure the development and production of oilfield. However, the borehole images obtained by video logging are too many interference items and the images are not clear. In order to better observe the state of three key target images of wellbore equipment: collar, leak and perforation, this paper proposes a detection model of key target images of video logging based on Googlenet network. The three key target images obtained from video logging are preprocessed as the input of neural network, and the three key target images are processed by Googlenet network. The multi-scale expression of image features is realized, and the detection accuracy of key target images in wellbore is improved. The experimental data in this paper used the image data set of a certain oilfield in Sichuan obtained by Eagle Eye downhole TV, and the experiment showed that the accuracy of the model in key image detection of wellbore was significantly improved compared with the common neural network VGG16 model and AlexNet model, its F1-Measure was significantly improved.
A New Method of Wave Parameter Retrieve Based on Transformer

A new method using deep learning to retrieve the main parameters of ocean waves which include significant wave heights, peak wave periods, peak wave lengths and peak wave directions. At present, retrieve of significant wave height can use the model of Convolutional Long Short-Term Memory(ConvLSTM) with attention. However, the accuracy of this method needs to be improved, and it can only retrieve a single wave parameter. We propose a Transformer-based network structure, which improves the accuracy effectively and can simultaneously retrieve multiple wave parameters. Finally, we use Pearson's correlation coefficient as an evaluation index to compare with the baseline. Experimental results show that the performance of the network has been improved.