Reviewerly/exHarmony huggingface dataset:
Image Encryption Based on Chua Chaotic Oscillator

With the rise of communication technology, there is a massive amount of data transmitting over the internet and network communication channels. Different encryption and decryption algorithms are being proposed to protect data over the internet. Chaotic oscillators are known to be one of the common sources of entropy in cryptography. In this paper, an algorithm scheme based on Chua chaotic oscillator is used to encrypt and decrypt images. The scheme contains both positions permutation using the chaotic key, and value transformation techniques using XOR bitwise operator. The encryption scheme is effective in small and large images. The paper examines 6 different sizes of images in regard to the computational time, correlation between adjacent pixels, and the entropy of the ciphered image.
Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality

Statistical analysis is the tool of choice to turn data into information and then information into empirical knowledge. However, the process that goes from data to knowledge is long, uncertain, and riddled with pitfalls. To be valid, it should be supported by detailed, rigorous guidelines that help ferret out issues with the data or model and lead to qualified results that strike a reasonable balance between generality and practical relevance. Such guidelines are being developed by statisticians to support the latest techniques for Bayesian data analysis. In this article, we frame these guidelines in a way that is apt to empirical research in software engineering. To demonstrate the guidelines in practice, we apply them to reanalyze a GitHub dataset about code quality in different programming languages. The dataset’s original analysis [Ray et al. 55 ] and a critical reanalysis [Berger et al. 6 ] have attracted considerable attention—in no small part because they target a topic (the impact of different programming languages) on which strong opinions abound. The goals of our reanalysis are largely orthogonal to this previous work, as we are concerned with demonstrating, on data in an interesting domain, how to build a principled Bayesian data analysis and to showcase its benefits. In the process, we will also shed light on some critical aspects of the analyzed data and of the relationship between programming languages and code quality—such as the impact of project-specific characteristics other than the used programming language. The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state-of-the-art while highlighting the boundaries of its validity. The guidelines can support building solid statistical analyses and connecting their results. Thus, they can help buttress continued progress in empirical software engineering research.
Machine Learning based Classification of Online News Data for Disaster Management

The exponential escalation of disaster loss in our country has led to the awareness that disaster risks are presumably increasing. As per statistics, India has confronted 371 natural hazards over the past few decades and severe casualties, infrastructural, agricultural and economic damages were recorded [1]. Credible and real time data such as news content are accessible liberally in legitimate websites and its analysis may provide assistance in administering hazard emergencies, preparedness and relief efficiently. On this grounds, a data scraping approach is proposed to gather hazard relevant news stories from the web by building a crawler software and incorporate machine learning approaches to filter out insightful information. The developed crawler software visits news reporting web pages and extracts news stories related to hazards. News illustrations are often unstructured as it includes less newsworthy content such as author’s opinions, interview responses and past studies. Hence, a supervised learning based text classification is performed to classify newsworthy content from news articles and approximately 70 percent accuracy was achieved.
Character-Level Transformer-Based Neural Machine Translation

Neural machine translation (NMT) is nowadays commonly applied at the subword level, using byte-pair encoding. A promising alternative approach focuses on character-level translation, which simplifies processing pipelines in NMT considerably. This approach, however, must consider relatively longer sequences, rendering the training process prohibitively expensive. In this paper, we discuss a Transformer-based approach, that we compare, both in speed and in quality to the Transformer at subword and character levels, as well as previously developed character-level models. We evaluate our models on 4 language pairs from WMT'15: DE-EN, CS-EN, FI-EN and RU-EN. The proposed architecture can be trained on a single GPU and is 34% faster than the character-level Transformer; still, the obtained results are at least on par with it. In addition, our proposed model outperforms the subword-level model in FI-EN and shows close results in CS-EN. To stimulate further research in this area and close the gap with subword-level NMT, we make all our code and models publicly available.
How to Track Your Dragon: A Multi-attentional Framework for Real-Time RGB-D 6-DOF Object Pose Tracking

We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects. Such a problem poses multiple challenges originating both from the objects’ nature and their interaction with their environment, which previous approaches have failed to fully address. The proposed framework encapsulates methods for background clutter and occlusion handling by integrating multiple parallel soft spatial attention modules into a multitask Convolutional Neural Network (CNN) architecture. Moreover, we consider the special geometrical properties of both the object’s 3D model and the pose space, and we use a more sophisticated approach for data augmentation during training. The provided experimental results confirm the effectiveness of the proposed multi-attentional architecture, as it improves the State-of-the-Art (SoA) tracking performance by an average score of 34.03 $$\%$$ for translation and 40.01 $$\%$$ for rotation, when tested on the most complete dataset designed, up to date, for the problem of RGB-D object tracking. Code will be available in: https://github.com/ismarou/How_to_track_your_Dragon .
Assessing partially ordered clustering in a multicriteria comparative context

This study considers the task of clustering for data characterized by peculiar quantitative features in that they express performance according to different indicators or criteria. Performance is supposed to be optimized in one way or the other, i.e. maximized or minimized. This peculiar type of data introduces a comparative context that is not generally taken into account in the field of pattern recognition, in general, and clustering, in particular. In the present study, we introduce different concepts and develop tools that facilitate the evaluation of data partitions in this comparative context leading to the consideration of asymmetric preference relationships between objects and between clusters. We show their usefulness on the basis of artificial data and also by analyzing the results produced on real data by means of clustering methods.
Causal neural fuzzy inference modeling of missing data in implicit recommendation system

Implicit feedback data has many interaction form, such as clicking, collection and play count. These interactions can be tracked automatically, which can alleviate the problem of data sparsity caused by the difficulty of obtaining explicit ratings. Therefore, implicit recommendation attracts many scholars’ attentions. However, the uncertainty of the implicit feedback data takes a great challenge to the recommendation. Especially for the negative feedback (missing data), we could not be sure whether the users dislike or just have not seen the items. If we regard them as negative feedback, it may lead to bias of predictions. In this paper, we propose Causal Neural Fuzzy Inference (CNFI) algorithm to model the missing data in implicit recommendation. First, we give the mixed recommendation model based on CNFI. Specially, we analyze the impact factors of users exposure to items from causal perspective. Technically, fuzzy set theory is used for representing the impact factors. Causal neural fuzzy inference network is applied to learn the weights of fuzzy rules based on the fuzzy representation of impact factors. Finally, CNFI and Matrix Factorization (MF) perform joint learning to make recommendations. Extensive experiments on three realistic datasets verify the effectiveness and advancement of the proposed algorithm.
Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease

The progression of Alzheimer's disease (AD) is a continuous process in cognitive and biomarker changes, with only a fraction of mild cognitive impairment (MCI) patients eventually advancing to AD. There is no definite biomarker signature to determine this eventual progress. The discovery and development of prognostic biomarker signatures for AD is essential to address the challenges of AD drug discovery and development. The deep learning (DL) technique is a recent breakthrough in data science that enables researchers to discover previously unknown features comprised by complicated patterns learnt from large datasets. It has outperformed many traditional machine learning methods in computer vision tasks. In this article, we evaluated the performance of DL algorithms in differentiating patients with diagnosis of AD, MCI, or no evidence of dementia using baseline MRI data of the brain, and integrated features extracted from the neural network with other baseline biomarkers to develop an AD prognostic signature. This signature can be used to understand the patient heterogeneity in a study cohort and provide enrichment strategies for AD clinical trial design.
Visual relationship detection with region topology structure

Visual relationship detection is crucial for scene understanding of images, which aims to detect objects in the image and classify the visual relation for each pair of objects. In addition, it builds a bridge between computer vision and natural language. Current studies transform the visual relationship detection task into a classification task between detected objects, and propose various models which integrate vision feature, position feature and semantic features of certain image regions. Following this manner, our proposed method also extracts features from these three cues. Furthermore, it is often neglected that the topology between regions plays an important role in capturing different visual relationships. In order to take advantage of topology, we treat the region visual features as vertexes and construct a visual region graph, and we model the dependency between (subject,object) pairs with the weighted edges. We further propose a visual relation detection framework based on the regional topology structure, which enables the model to incrementally aggregate topology structure information. We evaluate our method on VRD dataset and VG dataset, the results of the proposed method are close to even higher than that of the state-of-art methods on some evaluation metrics.
Information Reuse and Stochastic Search

Many software systems operate in environments of change and uncertainty. Techniques for self-adaptation allow these systems to automatically respond to environmental changes, yet they do not handle changes to the adaptive system itself, such as the addition or removal of adaptation tactics. Instead, changes in a self-adaptive system often require a human planner to redo an expensive planning process to allow the system to continue satisfying its quality requirements under different conditions; automated techniques must replan from scratch. We propose to address this problem by reusing prior planning knowledge to adapt to unexpected situations. We present a planner based on genetic programming that reuses existing plans and evaluate this planner on two case-study systems: a cloud-based web server and a team of autonomous aircraft. While reusing material in genetic algorithms has been recently applied successfully in the area of automated program repair, we find that naively reusing existing plans for self- * planning can actually result in a utility loss. Furthermore, we propose a series of techniques to lower the costs of reuse, allowing genetic techniques to leverage existing information to improve utility when replanning for unexpected changes, and we find that coarsely shaped search-spaces present profitable opportunities for reuse.
Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs

Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a growing demand for hardware accelerators that accommodate a variety of CNNs to improve their inference latency and energy efficiency, in order to enable their deployment in real-time applications. Among popular platforms, field-programmable gate arrays (FPGAs) have been widely adopted for CNN acceleration because of their capability to provide superior energy efficiency and low-latency processing, while supporting high reconfigurability, making them favorable for accelerating rapidly evolving CNN algorithms. This article introduces a highly customized streaming hardware architecture that focuses on improving the compute efficiency for streaming applications by providing full-stack acceleration of CNNs on FPGAs. The proposed accelerator maps most computational functions, that is, convolutional and deconvolutional layers into a singular unified module, and implements the residual and concatenative connections between the functions with high efficiency, to support the inference of mainstream CNNs with different topologies. This architecture is further optimized through exploiting different levels of parallelism, layer fusion, and fully leveraging digital signal processing blocks (DSPs). The proposed accelerator has been implemented on Intel's Arria 10 GX1150 hardware and evaluated with a wide range of benchmark models. The results demonstrate a high performance of over 1.3 TOP/s of throughput, up to 97% of compute [multiply-accumulate (MAC)] efficiency, which outperforms the state-of-the-art FPGA accelerators.
A Literature Review on Mining Cyberthreat Intelligence from Unstructured Texts

Cyberthreat defense mechanisms have become more proactive these days, and thus leading to the increasing incorporation of cyberthreat intelligence (CTI). Cybersecurity researchers and vendors are powering the CTI with large volumes of unstructured textual data containing information on threat events, threat techniques, and tactics. Hence, extracting cyberthreat-relevant information through text mining is an effective way to obtain actionable CTI to thwart cyberattacks. The goal of this research is to aid cybersecurity researchers understand the source, purpose, and approaches for mining cyberthreat intelligence from unstructured text through a literature review of peer-reviewed studies on this topic. We perform a literature review to identify and analyze existing research on mining CTI. By using search queries in the bibliographic databases, 28,484 articles are found. From those, 38 studies are identified through the filtering criteria which include removing duplicates, non-English, non-peer-reviewed articles, and articles not about mining CTI. We find that the most prominent sources of unstructured threat data are the threat reports, Twitter feeds, and posts from hackers and security experts. We also observe that security researchers mined CTI from unstructured sources to extract Indicator of Compromise (IoC), threat-related topic, and event detection. Finally, natural language processing (NLP) based approaches: topic classification; keyword identification; and semantic relationship extraction among the keywords are mostly availed in the selected studies to mine CTI information from unstructured threat sources.
Fast LTL-Based Flexible Planning for Dual-Arm Manipulation

In this paper, we propose a method for automatically generating object handling actions based on simple action definitions. The need to replace workers by robots is increasing, and, in fact, many research projects on robots have worked with simple motion definitions. Many applications are for mobile robots such as drones, however, and if such methods are applied directly to object handling, like a pick and place operation, it is necessary for humans to give detailed instructions. Hence, our contribution is to propose a model that simulates the real world with an augmented hybrid system that includes the states of objects. Then, it becomes possible to automatically generate robot motions with simple motion definitions and calculate them within a reasonable time. We demonstrate through computer simulation with a dual-arm robot that robot motions can be generated by simple definitions even if the environment changes to a certain degree.
Integrated level of service index for buried water infrastructure: Selection and development of performance indicators

Buried water infrastructure (BWI) services (drinking water, wastewater, and stormwater) are vital for sustainable urban development, which can be impacted by increasing densification needs. This study aims to develop an integrated level of service (ILOS) index to evaluate the performance of BWI underneath the urban densification. Development of the ILOS index involved identifying and selecting key performance indicators (PIs) under the pre-defined criteria and objectives. Initially, eighty-four PIs were identified through comprehensive literature. These PIs were reduced to sixty-two, based on experts’ judgment and feedback using three selection criteria and further grouped into a hierarchical structure. A hybrid approach of dynamic fuzzy cognitive maps and an ordered weighted average operator was adopted to aggregate PIs into the ILOS index using the bottom-up approach. The developed ILOS index was applied to four municipalities within British Columbia, Canada. The results show that all four municipalities need to significantly improve their BWI for sustainable growth and effective urban management. A sensitivity analysis was conducted to identify the most important PIs for the design of the ILOS index. Future research will be conducted to apply the proposed approach for better decision-making of growth scenarios, which will provide early warning changes in urban densification.
A Bilingual Multi-type Spam Detection Model Based on M-BERT

Spam has harassed Internet users for a long time, and how to detect spam accurately and efficiently is a critical problem. As yet, there are lots of research works proposed to detect spam, e.g., black and white lists, machine learning methods, and deep learning content-level measures, etc. Based on previous works, we find that most of methods' accuracy can reach 0.95 when they focus on one type and one language spam. Nevertheless, nowadays, people will receive spam messages of different types, different sources, and even different languages. Toward this, we develop a novel model, which is based on Google multilingual bidirectional encoder representations from transformers (M-BERT). Meanwhile, we design a brand new bilingual multi-type spam dataset to train our model. Particularly, we utilize optical character recognition (OCR) to extract text from image-based spam. Through the experiment, we find that the proposed model's accuracy can reach 0.9648, which outperforms the comparison models. In terms of time overhead, the proposed model only costs 0.3168 seconds per training step, which is an acceptable overhead. Therefore, these analysis results demonstrate that our approach can detect bilingual multi-type spam effectively.
Survey on mixed impulse and Gaussian denoising filters

This study presents a comprehensive survey on mixed impulse and Gaussian denoising filters which are applied to an image in order to gauge the effects of this type of noise combination and to then determine optimal ways that can overcome such effects. The random noise model considered in this survey is the combined effect of impulse (salt and pepper) and Gaussian noise. After describing the noise models, the denoising filters which are applied to the images are classified and explained according to their design structure, the type of filters they use, the noise level they could overcome, and the limitations they face. This survey covers all related denoising methods and provides an assessment of the strengths and practical limitations of the different classes of denoising filters.
A survey of recommendation systems based on deep learning

Abstract Faced with massive amounts of data, people may not be able to choose the items they like. The recommendation system came into being, and it has achieved a breakthrough for a long time. Using deep learning can mine the hidden attributes of users' items and integrate them well, bringing new changes to the recommendation system. This article describes the deep learning-based recommendation system and the traditional recommendation system, and analyzes their advantages and disadvantages.
An energy-aware virtual machine migration strategy based on three-way decisions

Virtual machine migration (VMM) is a crucial way to ensure load balancing and save energy consumption of cloud hosts. In most previous studies, they apply the same virtual machine migration strategy for overloaded hosts, which ignore the possible communication between the migrated VM, and other factors. Thus, they are prone to incur excessive network overhead. This paper proposes a virtual machine migration strategy based on the three-way decision (VMM-3WD) to save cloud hosts’ energy consumption while considering the network correlation between virtual machines. The strategy first is to classify hosts into overloaded hosts, regular load hosts, and under-loaded hosts based on their load situation. Then, different migration strategies are targeted developed for these three types of cloud hosts. Specifically, the approach migrates the VMs in under-loaded hosts to regular load hosts. And then, the approach further develops two thresholds to divides overloaded hosts into massively overloaded hosts, moderately overloaded, and lightly overloaded hosts. The migration decision of VMs in all stages pursuing the goal of reducing the energy consumption of the network during the migration process. The experimental results show that the proposed algorithm can reduce the energy consumption generated by virtual machine migration and data center (DC) energy consumption while meeting the SLA.
Shadow removal via dual module network and low error shadow dataset

Shadow removal is a challenging task as it requires us to recover common penumbra at the shadow boundary and trys not to alter the illumination of the non-shadow regions. In this work, we propose a novel strategy to achieve these goals based on Generative Adversarial Networks and design a dual-module architecture (DM-GAN) consisting of a mask generator, a matte generator, an umbra module, a penumbra module, and a boundary generator. The mask generator and matte generator first produce a shadow alpha mask and a shadow matte for input shadow image. Combined with this mask and matte, we employ the umbra module and penumbra module to generate an umbra removal image and a mask of the penumbra. Finally, we recover the penumbra based on the idea of image inpainting to obtain a boundary-smoothing result with less alteration at non-shadow regions. Besides, we construct a Low Error Shadow Dataset (LESD) with less error and more scenes to maintain the global illumination consistency between shadow and non-shadow regions. Extensive experiments demonstrate that our proposed DM-GAN can outperform other state-of-the-art methods.