Reviewerly/exHarmony huggingface dataset:
War: An Efficient Pre-processing Method for Defending Adversarial Attacks

Deep neural networks (DNNs) have achieved extraordinary successes in many fields such as image classification. However, they are vulnerable to adversarial examples generated by adding slight perturbations to the input images, leading incorrect classification results. Due to the serious threats of adversarial examples, it is necessary to find a simple and practical way to defend against adversarial attacks. In this paper, we present an efficient preprocessing method called War (WebP compression and resizing operation) for defending adversarial examples. WebP compression is first performed on the input sample to remove the imperceptible perturbations from the adversarial example. Then, the compressed image is appropriately resized to further destroy the specific structure of the adversarial perturbations. Finally, we can get a clean sample that can be correctly classified by the model. Extensive experiments show that our method outperforms the state-of-the-art defense methods. It can effectively defend adversarial attacks while ensure the classification accuracy on the normal samples drops slightly. Moreover, it only requires a particularly short pre-processing time.
Flexible Effective Sample Size Based on the Message Importance Measure

In Monte Carlo based importance sampling estimations, Effective Sample Size (ESS) is an important index of simulation efficiency, since ESS can measure the divergence between the target distribution and the proposal distribution effectively, and thus is widely used to decide whether resampling is needed or not. Among several well-known variants of ESS, the Shannon entropy based perplexity has been widely used. In this paper, however, we propose a new ESS function (E-MIM) by using the message importance measure (MIM) instead of Shannon entropy. We show that E-MIM satisfies all of the five conditions for ESS generalizations. We also propose an MIM based divergence and investigate its approximation to E-MIM. Moreover, we present a resampling threshold selection method for the ratio between E-MIM and the corresponding actual sample size. Finally, we investigate the performance of E-MIM and other ESS functions through numerical simulations. By a particle filter experiment, we show that E-MIM outperforms other ESS functions in terms of mean-squared error.
LOWER BOUNDS FOR PERIODS OF DUCCI SEQUENCES

A Ducci sequence is a sequence of integer $n$ -tuples obtained by iterating the map $$\begin{eqnarray}D:(a_{1},a_{2},\ldots ,a_{n})\mapsto (|a_{1}-a_{2}|,|a_{2}-a_{3}|,\ldots ,|a_{n}-a_{1}|).\end{eqnarray}$$ Such a sequence is eventually periodic and we denote by $P(n)$ the maximal period of such sequences for given odd $n$ . We prove a lower bound for $P(n)$ by counting certain partitions. We then estimate the size of these partitions via the multiplicative order of two modulo $n$ .
Teaching Systems Thinking as a Foundation of Scrum

Scrum is an agile project management methodology, founded on principles of a learning organization. The principles of a learning organization are systems thinking, personal mastery, mental models, shared vision, and team learning. However, if organizations follow the Scrum practices without understanding the underlying principles, Scrum does not work as well as intended. Ideally, the foundational principles should be learned together with the Scrum practices, but often teams learn the practices without the principles. We have successfully taught Scrum practices and principles together in a graduate course in software engineering, and found students were able to execute Scrum effectively. We propose how the principles can also be taught in an industrial setting where practitioners know Scrum practices but not the foundation.
FogTestBed: A Generic Architecture for Testbed for Fog-based Systems

In recent years, the growth of the Internet of Things (IoT) has pushed the advancement of computing service at the network edge. With many connected devices at the user end, applications regarded with IoT need enough computing resource as much as closer to the devices. In this regard, the new paradigm of Fog/Edge computing can significantly benefit these applications. Researchers or developers working in these domains often require various types of IoT devices as well as ample edge services to develop or test their methods and hypotheses. In this paper, we propose FogTestBed - an architecture to implement a Testbed as a service using the Fog node servers located near the users' networks. FogTestBed allows testbed owners to build their testbeds infrastructure at the network edge and provide secure and private access to the clients for running experiment or testing. FogTestBed also contains an incentive model for giving monetary benefits to the testbed owners. Finally, we implemented a prototype of the FogTestBed using laptops as Fog nodes and measure the performance.
Essence of Blockchain Technology in Wireless Sensor Network: A brief study

Wireless Sensor Network (WSN) has been ruling the sensing world since the time of its emergence. With the advancement of the sensor technology, the battery constraints of the Wireless Sensor Networks (WSNs) are drawing a huge research attention of the researchers. The limited battery constraints can be dealt if the routing algorithms implied to sensor network are energy efficient. As the sensor nodes communicate in the wireless medium, the security of the sensor nodes becomes imperative to consider. Therefore, to affirm security issues, the Blockchain technology can be introduced at the Cluster head levels of cluster-based routing algorithm. Consequently, the leisure information about each node can be used to authenticate the data. In this paper, we have presented a study of blockchain technology pertaining to WSNs. It will help the readers to select the suitable method for their research work from the reported studies encapsulated in this paper.
Forensic Imaging for Art Diagnostics. What Evidence Should We Trust?

Abstract Diagnostics digital images are often used to assess artworks. However, as all digital images they are also concerned by the issue of integrity. Computer vision techniques can be employed to obtain physical evidence of possible tampering. In this paper we explore the possibility to apply state of the art forensic algorithms to typical painting diagnostic images, taking into consideration real case studies. State of the art algorithms have been applied to genuine and modified diagnostic images to detect if, and how, forgeries of such images could be automatically detected and documented. To the best of our knowledge this is the first time that such investigation is made. Results of the aforementioned tests prove that automatic assessment of the integrity of diagnostic images is challenging and that there are no reliable solutions currently available.
Cryptocurrencies Price Prediction Using Weighted Memory Multi-channels

After the invention of Bitcoin and a peer to peer electronic cash system based on the blockchain, the market of cryptocurrencies increases rapidly and attracts substantial interest from investors and researchers. Cryptocurrencies price volatility prediction is a challenging task owing to the high stochasticity of the markets. Econometric, machine learning and deep learning models are investigated to tackle the stochastic financial prices fluctuation and to improve the prediction accuracy. Although the introduction of exogenous factors such as macro-financial indicators and blockchain information helps the model prediction more accurately, the noise and effects from markets and political conditions are difficult to interpret and modelling. Inspired by the evidence of strong correlations among cryptocurrencies examined in previous studies, we originally propose a Weighted Memory Channels Regression (WMCR) model to predict the daily close price of cryptocurrencies. The proposed model receives time series of several heavyweight cryptocurrencies price and learns the interdependencies of them by recalibrating the weights of each sequence wisely. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) components are exploited to establish memory and extract spatial and temporal features. Moreover, regularization methods including kernel regularizers and bias regularizers and Dropout method are exploited to improve the generalization ability of the proposed model. A battery of experiments are conducted in this paper. The results present that the WMCR model achieves the state-of-art performance and outperforms other baseline models.
Hand PointNet-based 3D Hand Pose Estimation in Egocentric RGB-D Images

Recently, understanding hand and object manipulation is an active topic in First-Person Vision (FPV) community. In this study, we present an initial study on estimating 3-D hand joints using the state-of-the-art neuronal network. We firstly propose a pre-processing step that is to separate hand regions from clustered background. We deploy the completed pipeline for estimating 3-D hand joints based on HandPointNet (HPN). HPN demonstrates the state-of-the-art hand pose estimation performances with depth data. We deploy a fine-tuning scheme to Hand PointNet (HPN) on the CVAR [1], UCI-EGO [2] datasets for 3D hand pose estimation. In the experimental results, we evaluate the estimated results using the pre-processing step to see the effectiveness of the proposed method. The results show that 3-D joint estimation errors are decreased comparing with the full hand data on different datasets as MSRA, NYU, ICVL. Particularly, we measure the estimation errors of missing, obscured data. The experimental results infer that, it is still existing a big gap between the results of un-occluded and occluded cases. Based on this initial study, we tend to investigate more deeply the techniques addressing the object occlusions or self-occlusions cases that make the current networks hard to localize hidden joints/parts of the hand.
Lightweight Cryptography Algorithms in IoT - A Study

There are numerous cryptographic calculations like AES (Advanced Encryption Standard), SHA-256(Secure Hash Algorithm), and RSA/Elliptic Curve, Methods work for frameworks which have sensible force preparing and memory capacities yet not for implanted frameworks and sensor networks. Nowadays a colossal piece of the individuals is utilizing low force contraptions, sensor systems, clinical thought and (IoT)) for this, the conventional calculations are not upheld Lightweight cryptography techniques are proposed to defeat the issues of customary cryptographic strategies. This contains imperatives identified with physical size, preparing necessities, and memory limitations. These research paper investigations the different encryption methods and features the upsides and downsides of each approach.
D-MSCD: Mean-Standard Deviation Curve Descriptor Based on Deep Learning

Curve feature description is an important issue in the field of image matching. In the past years, this problem has been studied mainly based on handcrafted methods. To conquer the disadvantages of low discrimination and weak robustness of curve feature description under complex conditions, a Mean-Standard Deviation Curve Descriptor based on Deep learning (D-MSCD) is proposed in this paper. Firstly, a large-scale curve feature dataset with 210,000 labeled curve image patches is constructed for training and testing. After longitudinally compressing the support areas of the curve in each image into the support areas of points, the mean and standard deviation image patches of each curve are obtained, then the curve image patch is uniquely represented. Secondly, a modified L2-Net(DSM) which is a network architecture with dilated convolution is constructed to improve the performance of curve descriptors, and the experimental results on the Brown dataset show the mean FPR95 value is reduced by 17.48%. Finally, the modified L2-Net(DSM) is trained on the large-scale curve feature dataset and the model of D-MSCD is obtained, it achieves the best matching performance in every image change, and the average matching performance on the Oxford dataset is improved by 13.09%. Experimental results demonstrate the proposed D-MSCD has better effectiveness than the traditional handcrafted curve descriptors.
Double Attention-based Deformable Convolutional Network for Recommendation

Data sparsity is one of the serious problems in recommender systems, which can be tremendously alleviated by making use of informative reviews and deep learning technologies. In this paper, we propose a Double Attention-based Deformable Convolutional Network (DADCN) for recommendation. In the proposed DADCN, two parallel deformable convolutional networks, which adopt the word-level and review-level attention mechanisms, are designed to flexibly extract the deep semantic features of both users and items from reviews. The combination of two parallel deformable convolutional networks with the word-level and review-level attention mechanisms helps to capture the representative user preferences and item attributes. Extensive experimental results on four real-world datasets demonstrate that the proposed DADCN outperforms the baseline methods.
The Max-Cut Decision Tree: Improving on the Accuracy and Running Time of Decision Trees

Decision trees are a widely used method for classification, both by themselves and as the building blocks of multiple different ensemble learning methods. The Max-Cut decision tree involves novel modifications to a standard, baseline model of classification decision tree construction, precisely CART Gini. One modification involves an alternative splitting metric, maximum cut, based on maximizing the distance between all pairs of observations belonging to separate classes and separate sides of the threshold value. The other modification is to select the decision feature from a linear combination of the input features constructed using Principal Component Analysis (PCA) locally at each node. Our experiments show that this node-based localized PCA with the novel splitting modification can dramatically improve classification, while also significantly decreasing computational time compared to the baseline decision tree. Moreover, our results are most significant when evaluated on data sets with higher dimensions, or more classes; which, for the example data set CIFAR-100, enable a 49% improvement in accuracy while reducing CPU time by 94%. These introduced modifications dramatically advance the capabilities of decision trees for difficult classification tasks.
Risk Assessments: A Weighted Score Approach to Improving Risk Management Decisions

Increases in security breaches have resulted in the need for organizations to identify, mitigate, and eliminate potential risks more than ever before. One of the most effective ways to heighten security awareness and reduce its risks is to conduct a thorough risk assessment and consistently audit identified risks. The subjective nature of risk assessments and the lack of a standardized rating scale, however, can produce various outcomes and calculations, leading to an inconsistent analysis of the identified risks. The unstructured risk assessment models and frameworks may lead some organizations to contribute more resources to risks that are of lesser importance than others. This paper will propose a new method to improve the standard risk assessment models. An original risk assessment calculation method will be introduced and analyzed using DeLone and Mclean's IS Success Model.
Massive MIMO Data Detection Using 1-dimensional Convolutional Neural Network

In this work, we explore the use of an adaptive one-dimensional convolutional neural network (1d-CNN) for the massive multiple-input multiple-output (MIMO) data detection. We construct datasets corresponding to 100's of base station antennas serving 10's of transmit antennas and train an efficient detector by employing the feature extraction ability of CNNs. To improve the performance and be able to detect under the randomly varying channel scenario, we employ a data augmentation approach based on the existing computationally cheaper detectors. Our method is simple, and a non-iterative which works by unfolding the potential of deep network layers. We will show through simulation that the proposed method can significantly improve the learning under the randomly varying channel condition, and able to achieve competitive performances compared to the traditional detectors.
Text line segmentation based on local baselines and connected component centroids for Tibetan historical documents

Abstract Text line segmentation is one of the key contents in document image analysis and recognition. Due to the complex situation of text images in Tibetan historical document, such as the coexistence of slanting and distortion of text lines, especially the adhesion between lines, text line segmentation has become a challenging task. In this paper, a new method of text segmentation for Tibetan historical document is proposed. This method first obtains the local baseline information of the text line, and then performs the detection and segmentation of the adhesion area. Finally, according to the barycentre coordinates of the connected component and the local baseline information, each connected component is assigned to the corresponding text line. The method avoids the effect of text line distortion on the segmentation of the stuck text line and greatly reduces the error rate of the adhesion text line segmentation and can effectively deal with the segmentation of slanted and distorted Tibetan sticky text lines.
Dynamics of a periodic XY chain coupled to a photon mode

We study the real-time dynamics of a periodic $XY$ system exposed to a composite field comprised of a constant homogeneous magnetic and a quantized circularly polarized electromagnetic fields. The interaction between the quantized mode and spin-magnetic moments is modeled by the Dicke Hamiltonian. The rotating wave approximation is applied and the conditions for its validity are discussed. It is shown that if initially all of the excitations are contained in the field, then in the regime of large detuning, the main evolutionary effect involves oscillations of the excitations between the zero-momentum modes of the chain and the field. Accordingly, the reduced photon number and magnetization per site reveal a sort of oscillatory behavior. Effective Hamiltonians describing the short-time dynamics of the present model for small number of excitations and large detuning are introduced. The resonance case is considered in the context of photon emission from the chain initially prepared in the (partially) excited state. In particular, it is demonstrated, in the framework of a specific example, that the superradiant behavior shows up at the beginning of the emission, when we have an initial state with a maximally excited $XY$ chain. Possible applications of the model to problems such as spin chain and $J$-aggregate in a single-mode cavity are discussed.
BrainHood: Designing a cognitive training system that supports self-regulated learning skills in children

BACKGROUND: There is strong evidence that cognitive skills and executive functions are skills that children need in order to successfully learn in school. Although executive function disorders are not considered a learning disability, weaknesses in executive functioning are often observed in students with learning disabilities or ADHD. Cognitive games are a type of educational games which focus on enhancing cognitive functioning in children with different profiles of cognitive development, including students with neurocognitive and/or learning disabilities. Self-regulation and metacognitive skills also play an important role in academic performance. OBJECTIVE: In this work, we highlight the need of monitoring and supporting metacognitive skills (self-regulation) in the context of a cognitive training game. We propose a system for self-regulated cognitive training for children which supports metacognitive strategies allowing the child to reflect on their own progress, weaknesses and strengths, self-arrange the training content, and thus to promote their self-regulated learning skills. METHODS: We provide a narrative review of research in cognitive training, self-regulated learning and explainable recommendation systems for children in educational settings. RESULTS AND CONCLUSIONS: Based on the review, an experimental testbed is proposed to explore how transparency, explainability and persuasive strategies can be used to promote self-regulated learning skills in children, considering individual differences on learning abilities, preferences, and needs.
Smart-Contract Enabled Decentralized Identity Management Framework for Industry 4.0

Industry 4.0 heavily uses connected machine paradigm to automate, track and maintain processes. However, a major challenge in Industrial Internet-of-Things (IIoT) remains managing the burgeoning number of sensors. The centralized framework for managing these devices is raising concerns. In this work, we propose a novel proof-of-concept framework for managing identity and access management policies for IIoT. The new framework is a smart contract enabled and blockchain based decentralized life-cycle and access management system. It is backed by decentralized storage technology InterPlanatery FileSystem (IPFS). Our framework is decentralized and scalable unlike the state-of-the-art IIoT management frameworks designed for clouds which are centralized.
Regulating online content in East Africa: Potential challenges and possible solutions

This article identifies the key but common challenges in the regulation of online content in Kenya, Uganda and Tanzania in East Africa (EA), both in terms of the regulatory challenges and the controversies surrounding government legislative responses to curbing illegal and harmful online content. To address these challenges, the article proposes the incorporation of digital literacy into the existing cyber legislations through content regulation and digital literacy, a new concept geared towards empowering the internet users in EA to be not only aware of the existing content legislation but also develop critical thinking skills and abilities to take action on illegal and harmful online content on a daily basis.
Interplay of Machine Learning and Software Engineering for Quality Estimations

In this era, the agile mindset has innovated the traditional software engineering (SE) process through the integration of <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DevOps</sub> flow engines, scrum iterations, and automation of continuous integration (CI) and continuous deployment (CD) cycles. However, the CI/CD integration requires manual code-revisions and refactoring at large scales. Recently, machine-learning (ML) is employed in SE that allows legacy codes to be highly dynamic, less coupling in related modules, automatic code versioning, and refactoring, with less coupling among related modules. However, over time, ML models tend to become bulky, with increasing monotonic losses during model training. To address this, SE techniques like code revisions are employed over ML codes to allow low-order training losses, that enables seamless and precise workflow structures. Motivated from the aforementioned discussions, the paper presents a systematic review of the close interplay of SE and ML and possible interactions in different applications. Suitable research questions and case studies are presented for possible adoption scenarios that depict the close ML-SE interplay share with each other, with the concluding remarks. The paper forms useful insights for ML engineers, data science practitioners, and SE quality estimators towards the building of efficient and scalable software solutions.