Reviewerly/exHarmony huggingface dataset:
Assume, Guarantee or Repair

We present Assume-Guarantee-Repair (AGR) – a novel framework which not only verifies that a program satisfies a set of properties, but also repairs the program in case the verification fails. We consider communicating programs – these are simple C-like programs, extended with synchronous communication actions over communication channels. Our method, which consists of a learning-based approach to assume-guarantee reasoning, performs verification and repair simultaneously: in every iteration, AGR either makes another step towards proving that the (current) system satisfies the specification, or alters the system in a way that brings it closer to satisfying the specification. We manage handling infinite-state systems by using a finite abstract representation, and reduce the semantic problems in hand – satisfying complex specifications that also contain first-order constraints – to syntactic ones, namely membership and equivalence queries for regular languages. We implemented our algorithm and evaluated it on various examples. Our experiments present compact proofs of correctness and quick repairs.
An Improved ID3 Classification Algorithm Based On Correlation Function and Weighted Attribute

ID3 decision tree algorithm is a supervised learning model based on calculating the information gain to select the best splitting attribute, which is the main factor to construct a decision tree. The process of calculating gain takes into consideration only a current condition attribute and decision attribute, and the other condition attributes cannot be used to measure the attribute importance. Because of the above problem, an improved ID3 takes into consideration the connection between the current condition attribute and the other conditions attributes. An experiment is presented to compare our improved algorithm with the traditional ID3 algorithm. Experiment results show that our improved algorithm provides a decision tree with less number of leaves and higher predictive accuracy.
Multi-View Visual Question Answering with Active Viewpoint Selection

This paper proposes a framework that allows the observation of a scene iteratively to answer a given question about the scene. Conventional visual question answering (VQA) methods are designed to answer given questions based on single-view images. However, in real-world applications, such as human-robot interaction (HRI), in which camera angles and occluded scenes must be considered, answering questions based on single-view images might be difficult. Since HRI applications make it possible to observe a scene from multiple viewpoints, it is reasonable to discuss the VQA task in multi-view settings. In addition, because it is usually challenging to observe a scene from arbitrary viewpoints, we designed a framework that allows the observation of a scene actively until the necessary scene information to answer a given question is obtained. The proposed framework achieves comparable performance to a state-of-the-art method in question answering and simultaneously decreases the number of required observation viewpoints by a significant margin. Additionally, we found our framework plausibly learned to choose better viewpoints for answering questions, lowering the required number of camera movements. Moreover, we built a multi-view VQA dataset based on real images. The proposed framework shows high accuracy (94.01%) for the unseen real image dataset.
BBS: Micro-Architecture Benchmarking Blockchain Systems through Machine Learning and Fuzzy Set

Due to the decentralization, irreversibility, and traceability, blockchain has attracted significant attention and has been deployed in many critical industries such as banking and logistics. However, the micro-architecture characteristics of blockchain programs still remain unclear. What's worse, the large number of micro-architecture events make understanding the characteristics extremely difficult. We even lack a systematic approach to identify the important events to focus on. In this paper, we propose a novel benchmarking methodology dubbed BBS to characterize blockchain programs at micro-architecture level. The key is to leverage fuzzy set theory to identify important micro-architecture events after the significance of them is quantified by a machine learning based approach. The important events for single programs are employed to characterize the programs while the common important events for multiple programs form an importance vector which is used to measure the similarity between benchmarks. We leverage BBS to characterize seven and six benchmarks from Blockbench and Caliper, respectively. The results show that BBS can reveal interesting findings. Moreover, by leveraging the importance characterization results, we improve that the transaction throughput of Smallbank from Fabric by 70% while reduce the transaction latency by 55%. In addition, we find that three of seven and two of six benchmarks from Blockbench and Caliper are redundant, respectively.
Content-based Image Retrieval Using Color Difference Histogram in Image Textures

The aim of content-based image retrieval system is finding similar images to the query image from a database based on its visual content. In this paper, a novel retrieval system based on human vision is proposed. A factor that has a high impact on the search process is a set of features which are used in. The recent studies emerged that the human eye system considers the image content, texture, and color properties more than other features. Therefore, to retrieve more precisely the images, features should be used that are close to the human eye system. In the current paper, at first, the texture is extracted from the images using the local binary patterns algorithm. After that, the color differences of two adjacent pixels with the same texture are calculated in the HSV color space. Afterward, the histogram is taken from the color difference values. The obtained features from the histogram can describe the visual content of the images in more detail. Finally, the effective features are selected based on their entropy value. The prominent advantage of the proposed method is the lack of implementation of segmentation, clustering, training, and any other method of machine learning, which requires a lot of processing and time. The method is evaluated on two standard Corel 10K and Corel 5K databases, and its retrieval rate is significantly improved compared to some recent methods.
Multi-view crowd congestion monitoring system based on an ensemble of convolutional neural network classifiers

Multi-view video surveillance is a highly valuable tool to ensure the safety of the crowd in large public space. By utilizing complementary information captured by multiple cameras, the issue of limited views and occlusion in single views can be addressed to gain better insight into the whole monitored space. However, multi-view surveillance has been widely applied to microscopic crowd analysis, for example pedestrian detection and tracking, while macroscopic level analysis, which deals with the whole crowd, has received little attention. We propose a multi-view framework for the generation of level of service maps, which are the most commonly used measure of congestion at macroscopic level, based on an ensemble of state-of-the-art Convolutional Neural Networks (CNNs). Several combination rules are compared and evaluated on two datasets, both in sparse and dense scenarios. Our results show that this fusion framework improves the accuracy of level of service map generation, from 83.2% to 89.8%, and eliminates blind spots in single views. Our framework is implemented on a 3 D GIS platform, which provides a suitable interface for multi-view crowd congestion management. The results of a loading test show that a maximum of 48 cameras can be processed at a map refresh rate of 2 seconds.
3-Level Residual Capsule Network for Complex Datasets

The Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. Deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Residual Networks ease the training and have shown evidence that they can give good accuracy with considerable depth. Residual Capsule Network [15] has put the Residual Network and Capsule Network together. Though it did well on simple dataset such as MNIST, the architecture can be improved to do better on complex datasets like CIFAR-10. This brings us to the idea of 3-Level Residual Capsule which not only decreases the number of parameters when compared to the seven-ensemble model, but also performs better on complex datasets when compared to Residual Capsule Network.
Architecture Proposal for Machine Learning Based Industrial Process Monitoring

In the context of Industry 4.0, an emerging trend is to increase the reliability of industrial process by using machine learning (ML) to detect anomalies of production machines. The main advantages of ML are in the ability to (1) capture non-linear phenomena, (2) adapt to many different processes without human intervention and (3) learn incrementally and improve over time. In this paper, we take the perspective of IT system architects and analyse the implications of the inclusion of ML components into a traditional anomaly detection systems. Through a prototype that we deployed for chemical reactors, our findings are that such ML components are impacting drastically the architecture of classical alarm systems. First, there is a need for long-term storage of the data that are used to train the models. Second, the training and usage of ML models can be CPU intensive and may request using specific resources. Third, there is no single algorithm that can detect machine errors. Fourth, human crafted alarm rules can now also include a learning process to improve these rules, for example by using active learning with a human-in-the-loop approach. These reasons are the motivations behind a microservice-based architecture for an alarm system in industrial machinery.
Learning to Communicate with Autoencoders: Rethinking Wireless Systems with Deep Learning

The design and implementation of conventional communication systems are based on strong probabilistic models and assumptions. These fixed and conventional communication theories exhibit limitations in the utilization of the limited spectrum resources and the complexity of optimization for emerging wireless applications. Currently, new generations of wireless systems supported by artificial intelligence can learn from the wireless spectrum data, and optimize their utilization to enhance their performance. In this paper, we describe how deep learning can be used to design an end-to-end communication system using an encoder to replace the transmitter tasks such as modulation and coding, and a decoder for the receiver tasks such as demodulation and decoding. This flexible design can capture channel impairments effectively and optimize the operations of the transmitter and receiver altogether. We evaluate the case of a single-antenna system, incorporating impairments in the channel layer of the auto encoder and evaluating the response of different neural network optimization algorithms.
Polynomial Equation Based Localization and Recognition Intelligent Vehicles Axis using Wireless Sensor in MANET

Vehicle need for the image security is the present time. The task of security is being dangled as there is an assortment of information and vehicle to verify as the various dimensions of information security is required depending on the vehicle and picture information type. Encryption is the greatest catalyst way to deal with assuring the security of pictures and vehicles over open systems. The proposed another open cryptosystem key depends on the polynomial condition. In this methodology has been wanted due to their fight over the cryptanalysis assaults. Parameter, for example, relationship coefficient, entropy, 12 shape parameter, histogram investigation is utilized for the productivity of our planned strategy. The theoretic and reenactment outcome gives a high amount of security. Hence, it is reasonable for down to earth use in secure correspondence.
Individual approach to knowledge control in learning management system

The article will focus on an individual approach to learning and knowledge control of students. The article describes the learning management system "Vector", its applicability for training in universities and corporate training. The issues of building individual development trajectories of each student from the point of view of receiving recommendations from the system for the study of new sections are considered. The article also considers the possibility of using cognitive maps to build these trajectories.
BCoal: Bucketing-Based Memory Coalescing for Efficient and Secure GPUs

Graphics Processing Units (GPUs) are becoming a de facto choice for accelerating applications from a wide range of domains ranging from graphics to high-performance computing. As a result, it is getting increasingly desirable to improve the cooperation between traditional CPUs and accelerators such as GPUs. However, given the growing security concerns in the CPU space, closer integration of GPUs has further expanded the attack surface. For example, several side-channel attacks have shown that sensitive information can be leaked from the CPU end. In the same vein, several side-channel attacks are also now being developed in the GPU world. Overall, it is challenging to keep emerging CPU-GPU heterogeneous systems secure while maintaining their performance and energy efficiency. In this paper, we focus on developing an efficient defense mechanism for a type of correlation timing attack on GPUs. Such an attack has been shown to recover AES private keys by exploiting the relationship between the number of coalesced memory accesses and total execution time. Prior state-of-the-art defense mechanisms use inefficient randomized coalescing techniques to defend against such GPU attacks and require turning-off bandwidth conserving techniques such as caches and miss-status holding registers (MSHRs) to ensure security. To address these limitations, we propose BCoal - a new bucketing-based coalescing mechanism. BCoal significantly reduces the information leakage by always issuing pre-determined numbers of coalesced accesses (called buckets). With the help of a detailed application-level analysis, BCoal determines the bucket sizes and pads, if necessary, the number of real accesses with additional (padded) accesses to meet the bucket sizes ensuring the security against the correlation timing attack. Furthermore, BCoal generates the padded accesses such that the security is ensured even in the presence of MSHRs and caches. In effect, BCoal significantly improves GPU security at a modest performance loss.
Breast Cancer Classification Using AdaBoost- Extreme Learning Machine

Extreme learning machine (ELM) is a new learning algorithm with single-hidden layer feed-forward neural network (SLFN). In this study, an ensemble of ELMs is used to predict breast cancers. To this end, an AdaBoost-based algorithm is proposed to adapt ELM in ensemble learning. A threshold value is defined for reweighting part of data that is misclassified. Based on the threshold-value, a tuning parameter is used. This parameter is defined as the inverse of the number of neurons in ELM's hidden-layer. Then, an experiment is conducted for tuning the number of neuron in hidden-layer. The results indicate that the proposed ensemble learning can effectively improves the recall and precision of the classification.
Hypercorrection as a By-product of Education

Abstract Prescriptive grammar rules are taught in education, generally to ban the use of certain frequently encountered constructions in everyday language. This may lead to hypercorrection, meaning that the prescribed form in one construction is extended to another one in which it is in fact prohibited by prescriptive grammar. We discuss two such cases in Dutch: the hypercorrect use of the comparative particle dan ‘than’ in equative constructions, and the hypercorrect use of the accusative pronoun hen ‘them’ for a dative object. In two experiments, high school students of three educational levels were tested on their use of these hypercorrect forms (nexp1 = 162, nexp2 = 159). Our results indicate an overall large amount of hypercorrection across all levels of education, including pre-university level students who otherwise perform better in constructions targeted by prescriptive grammar rules. We conclude that while teaching prescriptive grammar rules to high school students seems to increase their use of correct forms in certain constructions, this comes at a cost of hypercorrection in others.
Deep Architectures for Abstractive Text Summarization in Multiple Languages

Abstractive text summarization is the task of generating a novel summary given an article, not by merely extracting and selecting text to produce a summary, but by actually creating and understating the given text to produce a summary. LSTM seq2seq encoder-decoder with attention models have proved successful in this task, but they suffer from some problems. In this work, we would go through multiple models to try and solve these problems, beginning with simple seq2seq with attention models to going to Pointer-Generator, to using a curriculum learning approach called Scheduled-Sampling, till we reach the new approaches of combining reinforcement learning with seq2seq. We have applied these models on multiple datasets for multiple languages, English and Arabic. We have also introduced a new novel method of working with agglutinative languages, it is a preprocessing technique that is applied to the dataset which increases the relevancy of the vocabulary, which effectively increases the efficiency of the text summarization without modifying the models, we call this technique advanced cleaning, we have applied it to the Arabic dataset, and it can then be applied to any other agglutinative language. We have built these models in Jupiter notebooks to run seamlessly on Google colaboratory.
Over-Complete-Dictionary-Based Improved Spread Spectrum Watermarking Security

This letter presents a theoretical analysis of the security of two over-complete-dictionary-based improved spread spectrum (ISS) watermarking systems, i.e., subspace-ISS (sub-ISS) and random matching pursuit (RMP)-ISS, in comparison with the conventional orthogonal-transform-based ISS system. Considering Kerckhoffs' principle and the watermarked-only attack (WOA) scenario, we derive the associated decoding regions under brute force attack in which the adversary randomly draws probing keys until getting access to the watermarking channel. We first reveal that the sub-ISS system is as equally secure as the conventional ISS system in terms of message decoding, but the sensed key could provide computational security of the spreading key. More importantly, the RMP-ISS system is shown to be securer than the conventional ISS system for it being able to survive brute force attack. Based on the findings, we further propose an empirical design mechanism that optimizes the security of the RMP-ISS scheme.
Semiparametric mixtures of regressions with single-index for model based clustering

In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models are indeed special cases of the new models. Backfitting estimates and the corresponding modified EM algorithms are proposed to achieve optimal convergence rates for both parametric and nonparametric parts. We establish the identifiability results of the proposed two models and investigate the asymptotic properties of the proposed estimation procedures. Simulation studies are conducted to demonstrate the finite sample performance of the proposed models. Two real data applications using the new models reveal some interesting findings.
Hyper-Parameter Initialization for Squared Exponential Kernel-based Gaussian Process Regression

Hyper-parameter optimization is an essential task in the use of machine learning techniques. Such optimizations are typically done starting with an initial guess provided to hyper-parameter values followed by optimization (or minimization) of some cost function via gradient-based methods. The initial values become crucial since there is every chance for reaching local minimums in the cost functions being minimized, especially since gradient-based optimizing is done. Therefore, initializing hyper-parameters several times and repeating optimization to achieve the best solutions is usually attempted. Repetition of optimization can be computationally expensive when using techniques like Gaussian Process (GP) which has an O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) complexity, and not having a formal strategy to initialize hyper-parameter values is an additional challenge. In general, re-initialization of hyper-parameter values in the contexts of many machine learning techniques including GP has been done at random over the years; some recent developments have proposed some initialization strategies based on the optimization of some meta loss cost functions. To simplify this challenge of hyper-parameter initialization, this paper introduces a data-dependent deterministic initialization technique. The specific case of the squared exponential kernel-based GP regression problem is focused on, and the proposed technique brings novelty by being deterministic as opposed to random initialization, and fast (due to the deterministic nature) as opposed to optimizing some form of meta cost function as done in some previous works. Although global suitability of this initialization technique is not proven in this paper, as a preliminary study the technique's effectiveness is demonstrated via several synthetic as well as real data-based nonlinear regression examples, hinting that the technique may have the effectiveness for broader usage.