Reviewerly/exHarmony huggingface dataset:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path

Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards a global minimum. In this study, we establish the convergence of SGD to a global minimum for nonconvex optimization problems that are commonly encountered in neural network training. Our argument exploits the following two important properties: 1) the training loss can achieve zero value (approximately), which has been widely observed in deep learning; 2) SGD follows a star-convex path, which is verified by various experiments in this paper. In such a context, our analysis shows that SGD, although has long been considered as a randomized algorithm, converges in an intrinsically deterministic manner to a global minimum.
Developing Two Different Novel Techniques for Arabic Text Stemming

Stemming is used to produce stem or root of words.The process is vital to different research fields such as text mining, sentiment analysis, and text categorization, etc.Several techniques have been proposed to stemming Arabic text and among them, Khoja and light-10 stemmers are the most widely used.In this paper, we propose and evaluate two different stemming techniques to Arabic that are based on light stemming techniques.The new stemmers are compared to best reported light stemmer, which is light-10.Results and experiments, which were conducted using standard collections, reveal that The proposed stemmers yield 5.13% and 13.1% improvement in retrieval performance over light 10 with 0.369 average precision and 0.397, respectively and the improvement is statistically significant.
Enabling Data Markets Using Smart Contracts and Multi-party Computation

With the emergence of data markets, data have become an asset that is used as part of transactions. Current data markets rely on trusted third parties to manage the data, creating single points of failure with possibly disastrous consequences on data privacy and security. The lack of technical solutions to enforce strong privacy and security guarantees leaves the data markets’ stakeholders (e.g., buyers and sellers of data) vulnerable when they transact data. Smart Contracts and Multi-Party Computation represent examples of emerging technologies that have the potential to guarantee the desired levels of data privacy and security. In this paper, we propose an architecture for data markets based on Smart Contracts and Multi-Party Computation and present a proof of concept prototype developed to demonstrate the feasibility of the proposed architecture.
A Decidable Logic for Tree Data-Structures with Measurements

We present $${\textsc {Dryad}}_\textit{dec}$$ , a decidable logic that allows reasoning about tree data-structures with measurements. This logic supports user-defined recursive measure functions based on Max or Sum, and recursive predicates based on these measure functions, such as AVL trees or red-black trees. We prove that the logic’s satisfiability is decidable. The crux of the decidability proof is a small model property which allows us to reduce the satisfiability of $${\textsc {Dryad}}_\textit{dec}$$ to quantifier-free linear arithmetic theory which can be solved efficiently using SMT solvers. We also show that $${\textsc {Dryad}}_\textit{dec}$$ can encode a variety of verification and synthesis problems, including natural proof verification conditions for functional correctness of recursive tree-manipulating programs, legality conditions for fusing tree traversals, synthesis conditions for conditional linear-integer arithmetic functions. We developed the decision procedure and successfully solved 220+ $${\textsc {Dryad}}_\textit{dec}$$ formulae raised from these application scenarios, including verifying functional correctness of programs manipulating AVL trees, red-black trees and treaps, checking the fusibility of height-based mutually recursive tree traversals, and counterexample-guided synthesis from linear integer arithmetic specifications. To our knowledge, $${\textsc {Dryad}}_\textit{dec}$$ is the first decidable logic that can solve such a wide variety of problems requiring flexible combination of measure-related, data-related and shape-related properties for trees.
The Tale of Two Categories: Inductive groupoids and Cross-connections

A groupoid is a small category in which all morphisms are isomorphisms. An inductive groupoid is a specialised groupoid whose object set is a regular biordered set and the morphisms admit a partial order. A normal category is a specialised small category whose object set is a strict preorder and the morphisms admit a factorisation property. A pair of `related' normal categories constitutes a cross-connection. Both inductive groupoids and cross-connections were identified by Nambooripad \cite{mem,cross} as categorical models of regular semigroups. We explore the inter-relationship between these seemingly different categorical structures and prove a direct category equivalence between the category of inductive groupoids and the category of cross-connections.
A Data Mining Approach Applied to Wireless Sensor Neworks in Greenhouses

This research presents an innovative multi-agent system based on virtual organizations. It has been designed to manage the information collected by wireless sensor networks for knowledge discovery and decision making in greenhouses. The developed multi-agent system allowed us to take decisions on the basis of the analysis of the historical data obtained from sensors. The proposed approach improves the efficiency of greenhouses by optimizing the use of resources.
ZigBee Signal Phase Tracking Using a Discrete Kalman Filter Estimator under AWGN Channel

The objective of this research is to track the phase changes in Binary Phase Shift Keying (BPSK) modulated signal in ZigBee communication systems using discrete Kalman Filter (KF).Therefore, Kalman Filtering is used to estimate and optimize the carrier phase of BPSK modulated signal, in the presence of Additive White Gaussian Noise (AWGN) channel, by minimizing the phase deviation error.Therefore, a simulation model, using MATLAB, will be created to demonstrate ZigBee transmission system with the impact of the integrated filter.The expected results will show that Kalman Filter tracks the phase of BPSK modulated signal correctly and the performance of tracking will be measured by Mean Square Error (MSE) with respect to Signal to Noise Ratio (SNR).This study proposes a new method of phase tracking in ZigBee receivers in the presence of AWGN channel which can be extended to Internet of Things (IoT) applications.
In Codice Ratio: Machine Transcription of Medieval Manuscripts

Our project, In Codice Ratio, is an interdisciplinary research initiative for analyzing content of historical documents conserved in the Vatican Secret Archives (VSA). As most of such documents are digitized as images, Machine Transcription is both an enabler to the application of Knowledge Discovery techniques, as well as a useful tool to the paleographer for speeding up the transcription process. Our approach involves a convolutional neural network to recognize characters, statistical language models to compose and rank word transcriptions, and crowdsourcing for scalable training data collection. We have conducted experiments on pages from the medieval manuscript collection known as the Vatican Registers. Our results show that almost all the considered words can be transcribed without significant spelling errors.
Solving Double‐Sided Puzzles: Automated Assembly of Torn‐up Banknotes Evidence

Reconstruction of 2D object is a problem concerning many different fields such as forensics science, archiving, and banking. In the literature, it is considered as one-sided puzzle problem. But this study handles torn banknotes as a double-sided puzzle problem for the first time. In addition to that, a new dataset (ToB) is created for solving this problem. A selection approach based on the Borda count method is adopted in order to make the right decision as to which keypoint-based method is to be used in the proposed reconstruction system. The selection approach was determined the Accelerated-KAZE (AKAZE) as the most successful keypoint-based method. This study also proposes new measures determining the success ratio of the reconstructed banknotes and calculating their loss ratio. When the torn banknotes were reconstructed with the AKAZE-based reconstruction system, the average success rate was calculated as 95.55% by the proposed metric.
A bio metric based approach for using captcha – to enhance accessibility for the visually impaired

Aim: To propose a system for convenience in using CAPTCHA by the Visually Impaired person.Method: To make this access secure CAPTCHA ("Completely Automated Public Turing test to tell Computers and Humans Apart") was invented. This form of CAPTCHA requires that the user type the letters of a distorted image, sometimes with the addition of an obscured sequence of letters or digits that appears on the screen. This traditional method receives many criticism from the disable people. Hence a finger print based CAPTCHA is introduced.Result: A CAPTCHA MONSTER, a convenient system of breaking CAPTCHA for the Visually Impaired is Proposed.Conclusion: Thus in this proposed system, it is possible to access the web services on websites for various day to day applications by effective authentication procedure.
The E-library of the Central Scientific Agricultural Library: The features and structure

The technology of developing the Electronic Scientific Agricultural Library the E-library structure are discussed. The E-library comprises two databases: the Knowledge E-library and the Document Collections E-library (ELC) being developed in accordance with the general concept of the industry’s E-library. The ELC is an element of the Centralized Electronic Library System and is integrated into the minor cloud library and information system (MOBIS). MOBIS provides services for the participating libraries’ designing their own digital catalogs and electronic libraries. The document type composition, search options and several services are described. Inclusion of fulltext databases increases significantly its volume as well as the quality of user information services. The authors conclude that the Electronic Scientific Agricultural Library should supplement the library collection of printed materials and valuable documents, to widen access to the Library’s collections, to increase the user number and to deliver documents to any place. The Electronic Scientific Agricultural Library has been being developing and appended with new documents from its own collection and external sources.
Weight-based label-unknown multi-view data set generation approach

Multi-view learning aims to solve multi-view data set which consists of multiple instances with different views. Traditional multi-view learning approaches always encounter small-scale label-known multi-view instances problem and insufficient discriminant information problem. Although some label-unknown multi-view data set generation approaches are developed to enhance useful discriminant information, the weights of views and features are not considered. This paper proposes a weight-based label-unknown multi-view data set generation approach (WLM) to overcome such a disadvantage. The procedure of WLM consists of three main steps. First, get the weights of views and features. Second, get similar instances of each label-known instance. Third, generate and select feasible label-unknown instances which are applied to multi-view learning approaches along with the original label-known multi-view instances. Further, comparisons and analysis about classification performance, clustering performance, bipartite ranking performance, image retrieval performance, significance on some multi-view data sets with some multi-view learning approaches validate the usefulness of WLM.
Speaker-Phonetic I-Vector Modeling for Text-Dependent Speaker Verification with Random Digit Strings

This paper proposes a speaker-phonetic i-vector modeling method for text-dependent speaker verification with random digit strings, in which enrollment and test utterances are not of the same phrase. The core of the proposed method is making use of digit alignment information in i-vector framework. By utilizing force alignment information, verification scores of the testing trials can be computed in the fixed-phrase situation, in which the compared speech segments between the enrollment and test utterances are of the same phonetic content. Specifically, utterances are segmented into digits, then a unique phonetically-constrained i-vector extractor is applied to obtain speaker and channel variability representation for every digit segment. Probabilistic linear discriminant analysis (PLDA) and s-norm are subsequently used for channel compensation and score normalization respectively. The final score is obtained by combing the digit scores, which are computed by scoring individual digit segments of the test utterance against the corresponding ones of the enrollment. Experimental results on the Part 3 of Robust Speaker Recognition (RSR2015) database demonstrate that the proposed approach significantly outperforms GMM-UBM by 52.3% and 53.5% relative in equal error rate (EER) for male and female respectively.
Collaboration as Differentiation: Rethinking interaction intra-actively

This paper is a invitation to interaction designers across disciplines to rethink the shaping of interaction “intra-actively”. Whether in human-computer interaction design or interdisciplinary and interactive performance practices, we propose to shift the emphasis from interaction between things, towards the intra-active processes of differentiation by which such things are continually made and unmade. Expanding interaction design by engaging in processes intended to bring awareness to the value systems involved in the local production of “interaction” and “things that interact” offers an opportunity to treat these values, and likewise the designers (be it engineers or choreographers or composers), as objects themselves in the design process. In the traditions of feminist, new materialist, and process philosophy we weave a narrative of appropriated perspectives in order to dismantle hegemonic accounts of correlationism and representationalism in interaction design, while investigating the concepts of boundary objects, diffraction, and critical appropriation as potential approaches to intra-active design.
A system for generating complex physically accurate sensor images for automotive applications

We describe an open-source simulator that creates sensor irradiance and sensor images of typical automotive scenes in urban settings. The purpose of the system is to support camera design and testing for automotive applications. The user can specify scene parameters (e.g., scene type, road type, traffic density, time of day) to assemble a large number of random scenes from graphics assets stored in a database. The sensor irradiance is generated using quantitative computer graphics methods, and the sensor images are created using image systems sensor simulation. The synthetic sensor images have pixel level annotations; hence, they can be used to train and evaluate neural networks for imaging tasks, such as object detection and classification. The end-to-end simulation system supports quantitative assessment, from scene to camera to network accuracy, for automotive applications.
Video object segmentation via attention‐modulating networks

This Letter presents an attention-modulating network for video object segmentation that can well adapt its segmentation model to the annotated frame. Specifically, the authors first develop an efficient visual and spatial attention modulator to fast modulate the segmentation model to focus on the specific object of interest. Then they design a channel and spatial attention module and inject it into the segmentation model to further refine its feature maps. In addition, to fuse multi-scale context information, they construct a feature pyramid attention module to further process the top layer feature maps, achieving better pixel-level attention for the high-level feature maps. Finally, to address the sample imbalance issue in training, they employ focal loss that can distinguish simple samples from the difficult ones to accelerate the convergence of network training. Extensive evaluations on DAVIS2017 dataset show that the proposed approach has achieved state-of-the-art performance, outperforming the baseline OSMN by 3.6 and 5.4% in terms of IoU and F-measure without fine-tuning.
Sample preparation method for visualization of nanoparticulate captured on mixed cellulose ester filter media by enhanced darkfield microscopy and hyperspectral imaging

Abstract A significant hurdle in conducting effective health and safety hazard analysis and risk assessment for the nanotechnology workforce is the lack of a rapid method for the direct visualization and analysis of filter media used to sample nanomaterials from work environments that represent potential worker exposure. Current best‐known methods include transmission electron microscopy (TEM) coupled with energy dispersive x‐ray spectroscopy (EDS) for elemental identification. TEM‐EDS is considerably time‐, cost‐, and resource‐intensive, which may prevent timely health and safety recommendations and corrective actions. A rapid screening method is currently being explored using enhanced darkfield microscopy with hyperspectral imaging (EDFM‐HSI). For this approach to be effective, rapid, and easy, sample preparation that is amenable to the analytical technique is needed. Here, we compare the sample preparation steps for mixed cellulose ester (MCE) filter media specified in NIOSH Method 7400—Asbestos and Other Fibers by Phase Contrast Microscopy (PCM)—against a new method, which involves saturation of the filter media with acetone. NIOSH Method 7400 was chosen as a starting point since it is an established technique for preparing transparent MCE filters for optical microscopy. Limitations in this method led to the development and comparison of a new method. The new method was faster, easier, and rendered filters more transparent, resulting in improved visualization and analysis of nanomaterials via EDFM‐HSI. This new method is suitable for a rapid screening protocol due to its speed, ease of use, and the improvement in image acquisition and analysis.
Ant Colony Optimization of Interval Type-2 Fuzzy C-Means with Subtractive Clustering and Multi-Round Sampling for Large Data

Fuzzy C-Means (FCM) is widely accepted as a clustering technique.However, it cannot often manage different uncertainties associated with data.Interval Type-2 Fuzzy C-Means (IT2FCM) is an improvement over FCM since it can model and minimize the effect of uncertainty efficiently.However, IT2FCM for large data often gets trapped in local optima and fails to find optimal cluster centers.To overcome this challenge an Ant Colony-based Optimization (ACO) is proposed.Another challenge encountered is determining the number of clusters to perform clustering.Subtractive clustering (SC) is an efficient technique to estimate appropriate number of clusters.Though for large datasets the convergence rate of ACO and SC becomes high and thus, it becomes challenging to cluster data and evaluate correct number of clusters.To encounter the challenges of large dataset, Multi-Round Sampling (MRS) technique is proposed.IT2FCM-ACO with SC and MRS technique performs clustering on subsets of data and determines suitable cluster centers and cluster number.The obtained clusters are then extended to the entire dataset.This eliminates the need for IT2FCM to work on the complete dataset.Thus, the objective of this paper is to optimize IT2FCM using ACO algorithm and to estimate the optimal number of clusters using SC while employing MRS to handle the challenges of voluminous data.Results obtained from several clustering evaluation measures shows the improved performance of IT2FCM-ACO-MRS compared to ITFCM-ACO and IT2FCM.Speed up for different sample size of dataset is computed and is found that IT2FCM-ACO-MRS is ≈1-5 times faster than IT2FCM and IT2FCM-ACO for medium datasets whereas for large datasets it is reported to be ≈ 30-150 times faster.
The Efficacy of SHIELD under Different Threat Models

In this appraisal paper, we evaluate the efficacy of SHIELD, a compression-based defense framework for countering adversarial attacks on image classification models, which was published at KDD 2018. Here, we consider alternative threat models not studied in the original work, where we assume that an adaptive adversary is aware of the ensemble defense approach, the defensive pre-processing, and the architecture and weights of the models used in the ensemble. We define scenarios with varying levels of threat and empirically analyze the proposed defense by varying the degree of information available to the attacker, spanning from a full white-box attack to the gray-box threat model described in the original work. To evaluate the robustness of the defense against an adaptive attacker, we consider the targeted-attack success rate of the Projected Gradient Descent (PGD) attack, which is a strong gradient-based adversarial attack proposed in adversarial machine learning research. We also experiment with training the SHIELD ensemble from scratch, which is different from re-training using a pre-trained model as done in the original work. We find that the targeted PGD attack has a success rate of 64.3% against the original SHIELD ensemble in the full white box scenario, but this drops to 48.9% if the models used in the ensemble are trained from scratch instead of being retrained. Our experiments further reveal that an ensemble whose models are re-trained indeed have higher correlation in the cosine similarity space, and models that are trained from scratch are less vulnerable to targeted attacks in the white-box and gray-box scenarios.
Inovasi Perguruan Tinggi Raharja Dalam Era Disruptif Menggunakan Metodologi iLearning

Rapidly technological advancements have led to the emergence of a disruptive era, namely the innovation theory that was initiated by newcumbent, the publication of which threatened incumbent. The effect of this disruptive is a fundamentally significant and widespread technological innovation that changes the way human relations in various heresies is no exception to higher education. When viewed from a quantitative perspective, the growth is quite severe. However, if it is related to its quality, its development is worrying. Therefore, higher education must compete to change the learning system by following disruptive patterns in order to improve the quality of learning that will improve the quality of human resources. In this study there are 2 (two) methods, The results of this study present the readiness of Raharja University in the face of the disruptive era through iLearning. Where in the learning process includes 3 (three) things, called Rinfo, iDu and iMe.With this learning method, students become more innovative and critical thinking.