Reviewerly/exHarmony huggingface dataset:
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints

In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints. Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training. We analyze the following problem: a dataset, algorithm, and fixed resource budget, what is the best achievable performance? We focus on the number of optimization iterations as the representative resource. Under such a setting, we show that it is critical to adjust the learning rate schedule according to the given budget. Among budget-aware learning schedules, we find simple linear decay to be both robust and high-performing. We support our claim through extensive experiments with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation). We also analyze our results and find that the key to a good schedule is budgeted convergence, a phenomenon whereby the gradient vanishes at the end of each allowed budget. We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
Blockchain Technology in Supply Chain Management: Preliminary Study

Despite significant research, the supply chain management challenges still have a long way to go with respect to solving the issues such as management of product supply information, product lifecycle, transport history, etc. Given the recent rise of blockchain technology in various industrial sectors, our work explores the issues prevalent in each stage of the supply chain and checks their candidacy for the implementation using blockchain technology. The analysis is performed in terms of the characteristics of trust and decentralization with respect to forming a generalized framework. The main contribution of this work is to create a conceptual overview of the areas where blockchain integrates with supply chain management in order to benefit further research and development.
Sparse Black-Box Video Attack with Reinforcement Learning

Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To overcome this drawback, a few methods try to select some key frames and then perform attacks based on them. Unfortunately, their selection strategy is independent of the attacking step, therefore the resulting performance is limited. Instead, we argue the frame selection phase is closely relevant with the attacking phase. The key frames should be adjusted according to the attacking results. For that, we formulate the black-box video attacks into a Reinforcement Learning (RL) framework. Specifically, the environment in RL is set as the recognition model, and the agent in RL plays the role of frame selecting. By continuously querying the recognition models and receiving the attacking feedback, the agent gradually adjusts its frame selection strategy and adversarial perturbations become smaller and smaller. We conduct a series of experiments with two mainstream video recognition models: C3D and LRCN on the public UCF-101 and HMDB-51 datasets. The results demonstrate that the proposed method can significantly reduce the adversarial perturbations with efficient query times.
Classifying Political Arabic Articles Using Support Vector Machine with Different Feature Extraction

In the recent years, the number of web logs, and the amount of opinionated data on the World Wide Web, have been grown substantially. The ability to determine the political orientation of an article automatically can be beneficial in many areas from academia to security. However, the sentiment classification of web log posts (political web log posts in particular), is apparently more complex than the sentiment classification of conventional text. In this paper, a supervised machine learning with two feature extraction techniques Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) are used for the classification process. For investigation, SVM with four kernels for supervised machine learning have been employed. Subsequent to testing, the results reveal that the linear with TF achieved the results in accuracy of 91.935% also with TF-IDF achieved the 95.161%. The linear kernel was deemed the most suitable for our model.
Bee Foraging Algorithm Based Multi-Level Thresholding For Image Segmentation

Multi-level thresholding is one of the essential approaches for image segmentation. Determining the optimal thresholds for multi-level thresholding needs exhaustive searching which is time-consuming. To improve the searching efficiency, a novel population based bee foraging algorithm (BFA) for multi-level thresholding is presented in this paper. The proposed algorithm provides different flying trajectories for different types of bees and takes both single-dimensional and multi-dimensional search aiming to maintain a proper balance between exploitation and exploration. The bee swarm is divided into a number of sub-swarms to enhance the diversity. A neighbourhood shrinking strategy is applied to mitigate stagnation and accelerate convergence. Experiments have been performed on eight benchmark images using between-class variance as the thresholding criterion. The performance of the proposed algorithm is compared with some state-of-art meta-heuristic algorithms. The results show that BFA is efficient and robust, produces excellent results with few control parameters, and outperforms other algorithms investigated in this consideration on most of the tested images.
Ambiguity-Free Optical–Inertial Tracking for Augmented Reality Headsets

The increasing capability of computing power and mobile graphics has made possible the release of self-contained augmented reality (AR) headsets featuring efficient head-anchored tracking solutions. Ego motion estimation based on well-established infrared tracking of markers ensures sufficient accuracy and robustness. Unfortunately, wearable visible-light stereo cameras with short baseline and operating under uncontrolled lighting conditions suffer from tracking failures and ambiguities in pose estimation. To improve the accuracy of optical self-tracking and its resiliency to marker occlusions, degraded camera calibrations, and inconsistent lighting, in this work we propose a sensor fusion approach based on Kalman filtering that integrates optical tracking data with inertial tracking data when computing motion correlation. In order to measure improvements in AR overlay accuracy, experiments are performed with a custom-made AR headset designed for supporting complex manual tasks performed under direct vision. Experimental results show that the proposed solution improves the head-mounted display (HMD) tracking accuracy by one third and improves the robustness by also capturing the orientation of the target scene when some of the markers are occluded and when the optical tracking yields unstable and/or ambiguous results due to the limitations of using head-anchored stereo tracking cameras under uncontrollable lighting conditions.
Text Classification Using Long Short-Term Memory With GloVe Features

In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations with regard to large-scale dataset training. Deep Learning is a proposed method for solving problems in text classification techniques. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 95
SteelCore: an extensible concurrent separation logic for effectful dependently typed programs

Much recent research has been devoted to modeling effects within type theory. Building on this work, we observe that effectful type theories can provide a foundation on which to build semantics for more complex programming constructs and program logics, extending the reasoning principles that apply within the host effectful type theory itself. Concretely, our main contribution is a semantics for concurrent separation logic (CSL) within the F ⋆ proof assistant in a manner that enables dependently typed, effectful F ⋆ programs to make use of concurrency and to be specified and verified using a full-featured, extensible CSL. In contrast to prior approaches, we directly derive the partial-correctness Hoare rules for CSL from the denotation of computations in the effectful semantics of non-deterministically interleaved atomic actions. Demonstrating the flexibility of our semantics, we build generic, verified libraries that support various concurrency constructs, ranging from dynamically allocated, storable spin locks, to protocol-indexed channels. We conclude that our effectful semantics provides a simple yet expressive basis on which to layer domain-specific languages and logics for verified, concurrent programming.
Local gradient full-scale transform patterns based off-line text-independent writer identification

Handwriting based writer identification is one of the reliable components of behavioral biometrics. A huge effort has been done in recent years to improve the writer identification performance. Our paper presents a new and effective off-line text-independent system for writer identification. Extracting features from handwriting substantially impacts the ability of the classification process to identify the query writers. With the use of suitable classifier, a well-designed and discriminative feature extraction improves the classification performance. For that, we introduce a discriminative yet simple feature method, referred to as Local gradient full-Scale Transform Patterns (LSTP). The proposed LSTP algorithm captures salient local writing structure at small regions of interest of the writing. These writing regions are termed as connected components. In the classification stage, we perform Hamming distance based NN classifier to compare and match LSTP feature vectors. The proposed framework is evaluated on 9 well-known handwritten benchmarks. Experimental results show high identification performance against the current state-of-the-art.
Blockchain Behavioral Traffic Model as a Tool to Influence Service IT Security

The present article describes the behavioral model of blockchain services; their reliability is confirmed on the basis of experimental data. The authors identify the main technical characteristics and features associated with data transmission through the network. The authors determine the network scheme, working with blockchain transactions and the dependence of network characteristics on application parameters. They analyze the application of this model for the detection of the blockchain service and the possibility of the existing security mechanisms of this technology being evaded. Furthermore, the article offers recommendations for hiding the blockchain traffic profile to significantly complicate its identification in the data network.
Multiattribute decision making for the assessment of disaster resilience in the Three Gorges Reservoir Area

Disaster resilience assessments are critical to both urban and rural development, especially in disaster-prone regions.The Three Gorges Reservoir Area is a typical disaster-prone area, but there is a lack of research on disaster resilience in this area.We proposed a set of indicators and methodologies as a development of a comprehensive disaster resilience evaluation index system that encompasses societal, economic, natural environment, and infrastructure perspectives.Then an integrated approach, which combines 3 weight value determination methods, 6 evaluation methods, and 3 sorting methods, was developed and applied to disaster resilience assessments of 17 counties in the Three Gorges Reservoir Area, from which a final ranking was obtained.The conclusions we got from the research are: (1) the overall disaster resilience in the Three Gorges Reservoir Area is relatively low; (2) the disaster resilience in the Three Gorges Reservoir Area has significant regional differences, as well as social, economic, infrastructural, and environmental resilience; and (3) the societal, economic, and natural environment resilience all have a certain correlation with disaster resilience.Finally, suggestions were given to improve the disaster resilience in the Three Gorges Reservoir Area.This study can provide a framework and method for disaster resilience assessment.
An Agile-Based Integrated Framework for Mobile Application Development Considering Ilities

The development of mobile apps during the last decade has had a significant increase in its share of the software market.While there are specific characteristics that separate it from traditional software development, there is a lack of guidance on issues encountered during the mobile software development process.With the aim of defining a mobile application development framework that considers the specific characteristics of developing mobile apps, we started by carrying out a systematic mapping study of the software development process for mobile applications, then we administered a survey and completed a qualitative study with industry experts.These studies allowed us to identify the main trends in the software process for mobile apps, and to uncover the main challenges for app development.We have organized the findings in a framework that integrates the specific challenges of mobile development, which we call Mobile Ilities, with software development activities that are linked through an agile process.The results of early tests carried out with computer science students are encouraging.Our proposal has served as a guide for novice developers throughout the process of creating a final product, combining the existing knowledge of developers about Scrum with the specific characteristics of mobile development, and providing mechanisms to link these characteristics with the elements of the development process.
A Survey on Deep Convolutional Neural Networks for Image Steganography and Steganalysis

Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN).In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning.Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques.The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis.Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.
A New Approach of Multilevel Unsupervised Clustering for Detecting Replication Level in Large Image Set

Replicate and near-replicate images are variants derived from original images that are common among the large number of images on the cloud, desktop and mobile storage areas respectively. The existence of such near-replicate an image in web search indicates the presence of redundancy and lowers the system performance. The detection of replication is challenging due to the multitude of possible variations of images. In this paper, a new system called Multilevel Clustering for Image Replication Identification (MCIRI) is presented. The proposed MCIRI system aims to identify the replication level of images over the large storage area based on multilevel unsupervised clustering techniques. The MCIRI consists of four stages: feature extraction, first level clustering, second level clustering and replication measurement. In the first stage, the MCIRI extract the three features over each individual image in the image set based on standard statistical operations such as mean, standard deviation and variance. Next stage, the MCIRI split the image feature vector of image-set into ‘K’ dissimilar groups based on K-means clustering technique. In the third stage, the MCIRI partially identifies the dissimilar cluster over each individual cluster in the cluster set of previous level clustering result. In the final stage, the MCIRI estimate the replication level of each individual cluster in the result of second level clustering based on the cluster validation measure. Experimental results show that the proposed MCIRI is better suitable to estimate the replication level in the image set.
Self-organizing subspace clustering for high-dimensional and multi-view data

A surge in the availability of data from multiple sources and modalities is correlated with advances in how to obtain, compress, store, transfer, and process large amounts of complex high-dimensional data. The clustering challenge increases with the growth of data dimensionality which decreases the discriminate power of the distance metrics. Subspace clustering aims to group data drawn from a union of subspaces. In such a way, there is a large number of state-of-the-art approaches and we divide them into families regarding the method used in the clustering. We introduce a soft subspace clustering algorithm, a Self-organizing Map (SOM) with a time-varying structure, to cluster data without any prior knowledge of the number of categories or of the neural network topology, both determined during the training process. The model also assigns proper relevancies (weights) to different dimensions, capturing from the learning process the influence of each dimension on uncovering clusters. We employ a number of real-world datasets to validate the model. This algorithm presents a competitive performance in a diverse range of contexts among them data mining, gene expression, multi-view, computer vision and text clustering problems which include high-dimensional data. Extensive experiments suggest that our method very often outperforms the state-of-the-art approaches in all types of problems considered.
Model Repair with Quality-Based Reinforcement Learning.

Domain modeling is a core activity in Model-Driven Engineering, and these models must be correct.A large number of artifacts may be constructed on top of these domain models, such as instance models, transformations, and editors.Similar to any other software artifact, domain models are subject to the introduction of errors during the modeling process.There are a number of existing tools that reduce the burden of manually dealing with correctness issues in models.Although various approaches have been proposed to support the quality assessment of modeling artifacts in the past decade, the quality of the automatically repaired models has not been the focus of repairing processes.In this paper, we propose the integration of an automatic evaluation of domain models based on a quality model with a framework for personalized and automatic model repair.The framework uses reinforcement learning to find the best sequence of actions for repairing a broken model.
Convergence rates for the stochastic gradient descent method for non-convex objective functions

We prove the local convergence to minima and estimates on the rate of convergence for the stochastic gradient descent method in the case of not necessarily globally convex nor contracting objective functions. In particular, the results are applicable to simple objective functions arising in machine learning.
TacTok: semantics-aware proof synthesis

Formally verifying software correctness is a highly manual process. However, because verification proof scripts often share structure, it is possible to learn from existing proof scripts to fully automate some formal verification. The goal of this paper is to improve proof script synthesis and enable fully automating more verification. Interactive theorem provers, such as the Coq proof assistant, allow programmers to write partial proof scripts, observe the semantics of the proof state thus far, and then attempt more progress. Knowing the proof state semantics is a significant aid. Recent research has shown that the proof state can help predict the next step. In this paper, we present TacTok, the first technique that attempts to fully automate proof script synthesis by modeling proof scripts using both the partial proof script written thus far and the semantics of the proof state. Thus, TacTok more completely models the information the programmer has access to when writing proof scripts manually. We evaluate TacTok on a benchmark of 26 software projects in Coq, consisting of over 10 thousand theorems. We compare our approach to five tools. Two prior techniques, CoqHammer, the state-of-the-art proof synthesis technique, and ASTactic, a proof script synthesis technique that models proof state. And three new proof script synthesis technique we create ourselves, SeqOnly, which models only the partial proof script and the initial theorem being proven, and WeightedRandom and WeightedGreedy, which use metaheuristic search biased by frequencies of proof tactics in existing, successful proof scripts. We find that TacTok outperforms WeightedRandom and WeightedGreedy, and is complementary to CoqHammer and ASTactic: for 24 out of the 26 projects, TacTok can synthesize proof scripts for some theorems the prior tools cannot. Together with TacTok, 11.5% more theorems can be proven automatically than by CoqHammer alone, and 20.0% than by ASTactic alone. Compared to a combination of CoqHammer and ASTactic, TacTok can prove an additional 3.6% more theorems, proving 115 theorems no tool could previously prove. Overall, our experiments provide evidence that partial proof script and proof state semantics, together, provide useful information for proof script modeling, and that metaheuristic search is a promising direction for proof script synthesis. TacTok is open-source and we make public all our data and a replication package of our experiments.
2-hop Blockchain: Combining Proof-of-Work and Proof-of-Stake Securely

Bitcoin-like blockchains use a proof-of-work (PoW) mechanism, where security holds if the majority of the computing power is under the control of honest players. However, this assumption has been seriously challenged recently, and Bitcoin-like systems fail if this assumption is violated. In this work we propose a novel 2-hop blockchain protocol that combines PoW and proof-of-stake (PoS) mechanisms. Our analysis shows that the protocol is secure as long as the honest players control a majority of the collective resources (which consist of both computing power and stake). In particular, even if the adversary controls more than 50% of the computing power, security still holds if the honest parties hold sufficiently high stake in the system. As an added contribution, our protocol also remains secure against adaptive adversaries.