Reviewerly/exHarmony huggingface dataset:
Design and Optimization of a Fuzzy Logic System for Academic Performance Prediction

Currently, in Colombia, different problems in education exist; one of them is the inconvenience in tracing and controlling the learning trajectories that decide the topics taught in the country’s educational institutions. This work aims to implement a logic-based system that allows teachers and educational institutions to carry out a continuous monitoring process of students’ academic performance, facilitating early corrections of errors or failures in teaching methods, to promote educational support spaces within the educational institution.
Multi-view Convolutional Network for Crowd Counting in Drone-Captured Images

This paper proposes a novel lightweight and fast convolutional neural network to learn a regression model for crowd counting in images captured from drones. The learning system is initially based on a multi-input model trained on two different views of the same input for the task at hand: (i) real-world images; and (ii) corresponding synthetically created “crowd heatmaps”. The synthetic input is intended to help the network focus on the most important parts of the images. The network is trained from scratch on a subset of the VisDrone dataset. During inference, the synthetic path of the network is disregarded resulting in a traditional single-view model optimized for resource-constrained devices. The derived model achieves promising results on the test images, outperforming models developed by state-of-the-art lightweight architectures that can be used for crowd counting and detection.
Artificial Vision by Deep CNN Neocognitron

Deep convolutional neural networks (deep CNNs) show a large power for robust recognition of visual patterns. The neocognitron, which was first proposed by Fukushima (1979), is recognized as the origin of deep CNNs. Its architecture was suggested by the neurophysiological findings on the visual systems of mammals. It acquires the ability to recognize visual patterns robustly through learning. Although the neocognitron has a long history, improvements of the network are still continuing. This article discusses the recent neocognitron, focusing on differences from the conventional deep CNNs. Some other functions of the visual system can also be realized by networks extended from the neocognitron, for example, recognition of partly occluded patterns, the mechanism of selective attention, and so on.
A comment on “An evolutionary system of mineralogy: Proposal for a classification of planetary materials based on natural kind clustering”

Abstract The classification and nomenclature of mineral species is regulated by the Commission on New Minerals, Nomenclature and Classification of the International Mineralogical Association (IMACNMNC). This mineral species classification is necessary for Earth Sciences, as minerals constitute most planetary and interstellar materials. Hazen (2019) has proposed a classification of minerals and other Earth and planetary materials according to “natural clustering.” Although this classification is complementary to the IMA-CNMNC mineral classification and is described as such, there are some unjustified criticisms and factual errors in the comparison of the two schemes. It is the intent of the present comment to (1) clarify the use of classification schemes for Earth and planetary materials, and (2) counter erroneous criticisms or statements about the current IMA-CNMNC system of approving proposals for new mineral species and classifications.
Visual Abstraction of Geographical Point Data with Spatial Autocorrelations

Scatterplots are always employed to visualize geographical point datasets, which often suffer from an overdraw problem due to the increase of data sizes. A variety of sampling strategies have been proposed to reduce overdraw and visual clutter with the spatial densities of points taken into account. However, informative attributes associated with the points also play significant roles in the exploration of geographical datasets. In this paper, we propose an attribute-based abstraction method to simplify the cluttered visualization of large-scale geographical points. Spatial autocorrelations are utilized to measure the attribute relationships of points in local areas, and a novel attribute-based sampling model is designed to generate a subset of points to preserve both density and attribute characteristics of original geographical points. A set of visual designs and user-friendly interactions are implemented, enabling users to capture the spatial distribution of geographical points and get deeper insights into the attribute features across local areas. Case studies and quantitative comparisons based on the real-world datasets further demonstrate the effectiveness of our method in the abstraction and exploration of large-scale geographical point datasets.
Demystifying Cryptography behind Blockchains and a Vision for Post-Quantum Blockchains

One of the backbone technology of Blockchains is cryptography. In simpler terms cryptography is the mathematical art of secret writing, however it's applications are not limited to writing secret codes only. Essentially cryptography is used to achieve three main security goals namely confidentiality (in-formation must be hidden from the unintended users), integrity (information must be prevented from illicit modifications) and availability (information must be readily available to intended users at all times). In cryptography confidentiality is achieved by employing a technique called as Encryption, integrity is achieved by employing special one way functions called as Cryptographic Hash Functions and authentication (process for verifying identity of the sender) is achieved by employing a technique called as Digital Signatures. For better understanding about the working of Blockchain it is essential to understand cryptographic concepts on which Blockchain is built. This paper presents the brief overview of such cryptographic concepts. In particular this papers throws light on the cryptographic concepts used to build Bitcoin and Ethereum Blockchains. Furthermore this paper also throws light on the need and requirements of post-quantum cryptographic primitives for post-quantum Blockchains.
Adversarial and Adaptive Tone Mapping Operator for High Dynamic Range Images

This work addresses tone mapping, a common approach to convert high dynamic range (HDR) images into low dynamic range (LDR) images. We approach this problem by using adaptive tone mapping. We propose to deploy a conditional generative adversarial networks to build an adversarial and adaptive tone mapping operator (adTMO) that converts HDR into LDR images. We use an objective quality metric called the Tone Mapped Image Quality Index (TMQI) to evaluate our adTMO. Trained with 256&#x002A;256 images, adTMO is able to generate 256&#x002A;256 and high-resolution 1024&#x002A;2048 LDR images. Given 1024&#x002A;2048 HDR images, TMQI of the generated LDR images reaches the value of 0.90, which outperforms all other contemporary tone mapping operators.
Multi-label Classification of Commit Messages using Transfer Learning

Commit messages are used in the industry by developers to annotate changes made to the code. Accurate classification of these messages can help monitor the software evolution process and enable better tracking for various industrial stakeholders. In this paper, we present a state of the art method for commit message classification into categories as per Swanson's maintenance activities i.e. “Corrective”, “Perfective”, and “Adaptive”. This is a challenging task because not all commit messages are well written and informative. Existing approaches rely on keyword-based techniques to solve this problem. However, these approaches are oblivious to the full language model and do not recognize the contextual relationship between words. State of the art methodology in Natural Language Processing (NLP), is to train a context-aware neural network (Transformer) on a very large data set that encompasses the entire language and then fine-tunes it for a specific task. In this way, the model can learn the language, pay attention to the context, and then transfer that knowledge for better performance at the specific task. We use an off-the-shelf neural network called DistilBERT and fine-tune it for commit message classification task. This step is non-trivial because programming languages and commit messages have unique keywords, jargon, and idioms. This paper presents our effort in training this model and constructing the data set for this task. We describe the rules used to construct the data set. We validate our approach on industrial projects from GitHub, such as Kubernetes, Linux, TensorFlow, Spark, TypeScript, and PyTorch. We were able to achieve 87% F1-score for the commit message classification task, which is an order of magnitude accurate than previous studies.
Differentially Private Distributed Optimal Power Flow

Distributed algorithms enable private Optimal Power Flow (OPF) computations by avoiding the need in sharing sensitive information localized in algorithms sub-problems. However, adversaries can still infer this information from the coordination signals exchanged across iterations. This paper seeks formal privacy guarantees for distributed OPF computations and provides differentially private algorithms for OPF computations based on the consensus Alternating Direction Method of Multipliers (ADMM). The proposed algorithms attain differential privacy by introducing static and dynamic random perturbations of OPF sub-problem solutions at each iteration. These perturbations are Laplacian and designed to prevent the inference of sensitive information, as well as to provide theoretical privacy guarantees for ADMM sub-problems. Using a standard IEEE 118-node test case, the paper explores the fundamental trade-offs among privacy, algorithmic convergence, and optimality losses.
A Compact FPGA-Based Accelerator for Curve-Based Cryptography in Wireless Sensor Networks

The main topic of this paper is low-cost public key cryptography in wireless sensor nodes. Security in embedded systems, for example, in sensor nodes based on field programmable gate array (FPGA), demands low cost but still efficient solutions. Sensor nodes are key elements in the Internet of Things paradigm, and their security is a crucial requirement for critical applications in sectors such as military, health, and industry. To address these security requirements under the restrictions imposed by the available computing resources of sensor nodes, this paper presents a low-area FPGA-prototyped hardware accelerator for scalar multiplication, the most costly operation in elliptic curve cryptography (ECC). This cryptoengine is provided as an enabler of robust cryptography for security services in the IoT, such as confidentiality and authentication. The compact property in the proposed hardware design is achieved by implementing a novel digit-by-digit computing approach applied at the finite field and curve level algorithms, in addition to hardware reusing, the use of embedded memory blocks in modern FPGAs, and a simpler control logic. Our hardware design targets elliptic curves defined over binary fields generated by trinomials, uses fewer area resources than other FPGA approaches, and is faster than software counterparts. Our ECC hardware accelerator was validated under a hardware/software codesign of the Diffie-Hellman key exchange protocol (ECDH) deployed in the IoT MicroZed FPGA board. For a scalar multiplication in the sect233 curve, our design requires 1170 FPGA slices and completes the computation in 128820 clock cycles (at 135.31 MHz), with an efficiency of 0.209 kbps/slice. In the codesign, the ECDH protocol is executed in 4.1 ms, 17 times faster than a MIRACL software implementation running on the embedded processor Cortex A9 in the MicroZed. The FPGA-based accelerator for binary ECC presented in this work is the one with the least amount of hardware resources compared to other FPGA designs in the literature.
Proactive Serverless Function Resource Management

This paper introduces a new primitive to serverless language runtimes called freshen. With freshen, developers or providers specify functionality to perform before a given function executes. This proactive technique allows for overheads associated with serverless functions to be mitigated at execution time, which improves function responsiveness. We show various predictive opportunities exist to run freshen within reasonable time windows. A high-level design and implementation are described, along with preliminary results to show the potential benefits of our scheme.
AutoRetouch: Automatic Professional Face Retouching

Face retouching is one of the most time-consuming steps in professional photography pipelines. The existing auto-mated approaches blindly apply smoothing on the skin, destroying the delicate texture of the face. We present the first automatic face retouching approach that produces high-quality professional-grade results in less than two seconds. Unlike previous work, we show that our method preserves textures and distinctive features while retouching the skin. We demonstrate that our trained models generalize across datasets and are suitable for low-resolution cellphone images. Finally, we release the first large-scale, professionally retouched dataset with our baseline to encourage further work on the presented problem.
GuessWhich? Visual dialog with attentive memory network

Visual dialog is a task that two agents: Question-BOT (Q-BOT) and Answer-BOT (A-BOT), which communicate in natural language on the situation of information asymmetry. Q-BOT generates questions based on an image caption and a historical dialog. A-BOT answers the questions grounded on the image. Moreover, we play a cooperative ‘image guessing’ game between Q-BOT and A-BOT, so that Q-BOT can select an unseen image from a set of images. However, as the valid information of the image caption and the historical dialog fades along the interaction, existing methods usually generate irrelevant and homogenous questions, which are worthless to the visual dialog system. To tackle this issue, we propose an Attentive Memory Network (AMN) to fully exploit the image caption and historical dialog information. Specifically, the attentive memory network mainly consists of a memory network and a fusion module. The memory network holds long term historical dialog information and gives each round of the dialog a different weight. Aside from the historical dialog information, the fusion module in Q-BOT and A-BOT further uses the image caption and the image feature, respectively. The caption information assists Q-BOT with the attentive generation of the questions, and the image feature helps A-BOT produce precise answers. With the AMN, the generated questions are diverse and concentrated, and the corresponding answers are accurate. The experimental results on VisDial v1.0 show the effectiveness of our proposed model, which outperforms the state-of-the-art methods.
Evolutionary NAS with Gene Expression Programming of Cellular Encoding

The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme-symbolic linear generative encoding (SLGE)-simple, yet a powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via an evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the state-of-the-art handcrafted CNN architectures on CIFAR-10 and CFAR-100 image classification tasks; and achieves a competitive classification error rate with the existing NAS methods using fewer GPU resources.
Employing machine learning techniques to assess requirement change volatility

Abstract Lack of planning when changing requirements to reflect stakeholders’ expectations can lead to propagated changes that can cause project failures. Existing tools cannot provide the formal reasoning required to manage requirement change and minimize unanticipated change propagation. This research explores machine learning techniques to predict requirement change volatility (RCV) using complex network metrics based on the premise that requirement networks can be utilized to study change propagation. Three research questions (RQs) are addressed: (1) Can RCV be measured through four classes namely, multiplier, absorber, transmitter, and robust, during every instance of change? (2) Can complex network metrics be explored and computed for each requirement during every instance of change? (3) Can machine learning techniques, specifically, multilabel learning (MLL) methods be employed to predict RCV using complex network metrics? RCV in this paper quantifies volatility for change propagation, that is, how requirements behave in response to the initial change. A multiplier is a requirement that is changed by an initial change and propagates change to other requirements. An absorber is a requirement that is changed by an initial change, but does not propagate change to other requirements. A transmitter is a requirement that is not changed by an initial change, but propagates change to other requirements. A robust requirement is a requirement that is not changed by an initial change and does not propagate change to other requirements. RCV is determined using industrial data and requirement network relationships obtained from previously developed Refined Automated Requirement Change Propagation Prediction (R-ARCPP) tool. Useful complex network metrics in highest performing machine learning models are discussed along with the limitations and future directions of this research.
Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

These days, imbalanced datasets, denoted throughout the paper by ID, (a dataset that contains some (usually two) classes where one contains considerably smaller number of samples than the other(s)) emerge in many real world problems (like health care systems or disease diagnosis systems, anomaly detection, fraud detection, stream based malware detection systems, and so on) and these datasets cause some problems (like under-training of minority class(es) and over-training of majority class(es), bias towards majority class(es), and so on) in classification process and application.Therefore, these datasets take the focus of many researchers in any science and there are several solutions for dealing with this problem.The main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description (SVDD).There are naturally two kinds of resampling: Under-sampling (U-S) and oversampling (O-S).The O-S may cause the occurrence of over-fitting (the occurrence of over-fitting is its main drawback).The U-S can cause the occurrence of significant information loss (the occurrence of significant information loss is its main drawback).In this study, to avoid the drawbacks of the sampling techniques, we focus on the samples that may be misclassified.The data points that can be misclassified are considered to be the borderline data points which are on border(s) between the majority class(es) and minority class(es).First by SVDD, we find the borderline examples; then, the data resampling is applied over them.At the next step, the base classifier is trained on the newly created dataset.Finally, we compare the result of our method in terms of Area Under Curve (AUC) and F-measure and
RETRACTED: Privacy preserving and data transpiration in multiple cloud using secure and robust data access management algorithm

Nowadays, privacy preserving is playing important role in cloud computing where content based privacy is challengeable task in un-trusted cloud environment. Based on literature studies, the method has key complexities and access polices authentication issues. The system need to concentrate to bring strong encryption function and efficient key distribution polices to meet future challenges. The method need to address the real time application in cloud environments minimal computation cost, inherent defects in key management and flexible access control policy. Current approaches have still believe in user identity, mutual privacy and key agreement session wise among content owner, Trusted client, and cloud service provider. The Proposed work focused on designing a framework named Secure and Robust Data Access Management (SRDAM) Algorithm proposed to maintain enhanced privacy, secure data transportation, and data access managements. Proposed algorithm consolidates validating cloud service providers and after that considers the property necessity of cloud user and cloud service provider (CSP).Proposed SRDAM Algorithm reduces the 1.79 s Data uploading time (DUT), Data 1.80 s Downloading Time (DDT) and 11.02 s Communication Overhead (CO) for document, images and video for conventional methodologies.
Regional Attention Networks with Context-aware Fusion for Group Emotion Recognition

Group Emotion Recognition (GER) from images has many inherent challenges. Specifically, it is difficult to combine diverse emotions of different individuals into a single conclusive label. In addition, although utilization of information other than faces like scene and objects has proven helpful, it is still a challenge to effectively fuse predictions of individual sources. In this work, we proposed solutions to these two problems. First, we developed a regional attention mechanism to find important persons or objects, which play critical roles in the group emotion, and combine them based on importance. Second, we proposed a context-aware fusion mechanism to estimate weights from the image context to fuse different sources of information. Finally, we proposed to use a single backbone network to extract features from multiple sources, i.e., scene, faces, and objects, cutting down computation and memory cost. Experiments on two GER datasets have shown that the proposed framework achieves performance comparable to the state-of-the-art. Furthermore, a visualization study and a case study have demonstrated that the proposed model is effective to extract and more importantly, emphasize the most critical information in GER.
Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.