Reviewerly/exHarmony huggingface dataset:
Hiding All Labels for Multi-label Images: An Empirical Study of Adversarial Examples

Adversarial examples about deep learning models have been paid much attention in recent years, including single-label adversarial examples and multi-label adversarial examples. In this paper, for the first time, an empirical study of generating a multi-label adversarial example to hide all labels in a multi-label example is presented. The objective of hiding all labels in a multi-label example is to make deep learning models know nothing about the environments. That is very worthy of studying because deep learning models will say there is nothing, although the real input has more than one label. In the empirical study, we use five state-of-the-art multi-label attack algorithms, i.e., ML-CW, ML-DP, FGSM, MI-FGSM, MLA-LP, four popular datasets, i.e., VOC2007, VOC2012, NUS-WIDE and COCO, and two typical models ML-GCN and ASL for evaluation. We conduct extensive experiments and report the attack success rates, the amount of perturbations of the adversarial examples generated by state-of-the-art multi-label attack algorithms. We also report the attack performance when a typical defending algorithm based on JPEG compression is used. The work in this paper is beneficial to the future study of generating multi-label adversarial examples as well as defending them.
Are Crypto-Assets Green Enough? – An analysis of draft EU Regulation on markets in crypto assets from the perspective of the European Green Deal

In 2019 European Commission announced “The European Green Deal” a “a new growth strategy that aims to transform the EU into a fair and prosperous society, with a modern, resource-efficient and competitive economy where there are no net emissions of greenhouse gases in 2050 and where economic growth is decoupled from resource use”. The digital sector must also participate in the Green Deal effort. This articles analyzes questions of sustainability in the context of crypto assets, with particular emphasis on the question of whether Bitcon acutally represent a crypto asset, energy consumption, energy drain, the proof-of-work consensus protocol, the environmental footprint of crypto assets. The article concludes that Bitcoin’s current effect on environment remains controversial at best.
You Get What You Focus on: A Weighting Factor for IoU-based Regression Loss

Loss functions are essential to bounding box regression which plays a significant role in deep learning based object detection. Despite the effectiveness of the popular Intersection over Union (IoU) based losses, there is still an imbalance problem of high- and low-quality predicted bounding boxes, impeding the accuracy and convergence speed during bound box regression. Specifically, we observe that the huge amount of predicted bounding boxes having small overlapping regions with ground truth box overwhelms the amount of predicted bounding boxes having large overlapping regions. In this paper, we propose a simple weighting factor that is able to reshape the existing IoU-based losses according to a geometric relationship of bounding boxes. In this way, we are able to effectively down-weight the contribution of low-quality predicted boxes and focus training on high-quality ones. Extensive experiments have been carried out on popular IoU-based losses with various object detection techniques. By simply incorporating the proposed weighting factor, we are able to achieve notable performance gains on the popular MS COCO dataset.
Online Runtime Prediction Method for Distributed Iterative Jobs

Predicting the runtime of distributed iterative jobs can help reduce the deployment cost of clusters and optimize their resource allocation and scheduling strategies, but the runtime depends on various factors which are difficult to be acquired before execution. In this paper, we propose a generalized online prediction method for the runtime of distributed iterative jobs, which is centered on a series of online machine learning models. The method consists of three phases: 1) estimating the number of iterations for the current iterative job. 2) predicting the runtime metrics of each iteration by an online polynomial regression model. 3) Runtime metrics sequence is analyzed using an LSTM trained with online learning to predict the runtime of each iteration. We conducted experiments on typical Flink iterative jobs, and the experimental results show that our method improves the accuracy by 4.79% compared to the state-of-the-art methods, while for the improvement in accuracy for delta iterative jobs is even more than 15%.
Multi-Attribute Enhancement Network for Person Search

Person Search is designed to jointly solve the problems of Person Detection and Person Re-identification (Re-ID), in which the target person will be located in a large number of uncut images. Over the past few years, Person Search based on deep learning has made great progress. Visual character attributes play a key role in retrieving the query person, which has been explored in Re-ID but has been ignored in Person Search. So, we introduce attribute learning into the model, allowing the use of attribute features for retrieval task. Specifically, we propose a simple and effective model called Multi-Attribute Enhancement (MAE) which introduces attribute tags to learn local features. In addition to learning the global representation of pedestrians, it also learns the local representation, and combines the two aspects to learn robust features to promote the search performance. Additionally, we verify the effectiveness of our module on the existing benchmark dataset, CUHK-SYSU and PRW. Ultimately, our model achieves state-of-the-art among end-to-end methods, especially reaching 91.8% of mAP and 93.0% of rank-1 on CUHK-SYSU. Codes and models are available at https:// github. com/chenlq123/ MAE.
Four Input Sorter Good, Larger Ones Not So Good

Our own fresh results have suggested that the smallest size optimal sorting networks/graphs (which can be mapped onto hardware) might be the most appropriate entities (building blocks) for designing highly reliable computing systems. Sorting networks correspond to particular sorting algorithms, while their associated connectivity graphs can embody (minimal) two-terminal networks. Relying on these concepts one can associate a reliability polynomial to any sorting network. Here, we are going to extend on the work we have recently started on sorting networks by studying larger optimal sorting networks. Comparing the two-terminal reliability polynomials associated to these larger sorting connectivity graphs with those of Moore-Shannon hammocks of the same size will follow. In-depth comparisons will be done using both classical as well as novel figures-of-merit targeting the reliability enhancements of networks when performing computations. The results reported here will shed light on our previous findings, confirming that the optimal sorting network of four inputs is ideal for designing highly reliable computing systems, but also that its advantages, although of theoretical importance, are only marginal and fading quickly. In particular, the analyses of sorting networks with larger number of inputs reveals that hammocks are catching up with sorting networks at five and six inputs, and clearly overtaking sorting networks of seven inputs. All these simulations and detailed comparisons provide compelling arguments for why hammock networks are the ones we should rely upon (at more than one level) for 2D reliable computations (including the current quantum computing quest).
Design of die adjustment component vector decomposition method for metal screw forming

Abstract This research focuses on the punch case adjustment vector decomposition problem of metal screw forming machines that occurred frequently in fastener manufacturing. The dimension component vectors of screw forming center axial line misalign for die adjustment were often decided by human experience currently and the adjusted results were not always be satisfied. It is a difficult problem to analyze the punch die adjust component vector from screw specific shape measurement information. This research integrates rotation axial optical measurement instrument and die adjustment mechanical specification information of forming machine then using the proposed sampling process so that the adjustment component vectors with respect to the punch case can be decomposed. The result of this research provides the adjustment guidance that includes main items of misalignment component vector. A practical verification is also demonstrated in this paper by using a type of screw forming machine for showing the feasibility of the designed die adjustment method.
An Intense Study on Intelligent Service Provisioning for Multi-Cloud Based on Machine Learning Techniques

Cloud computing is evolving as a paradigm that models “everything-as-a-service.” Organizations are migrating from single cloud environment to multi-cloud environment for issuing streamlined services for multi-tenants. The lock-in effect is a scenario in which the consumers feel difficulty in migrating from one cloud to another without inconvenience which is solved by the deployment of multi-cloud. Service provisioning is the process of delivering the virtualized physical resources, applications, and infrastructure. There is a need for intelligent service provisioning (ISP) in multi-cloud because the cloud user faces difficulty in choosing the appropriate services from variety of services provided by a large number of cloud providers. By providing with appropriate cloud services to the user, the service level agreements (SLAs) are fulfilled. Quality of Service (QoS) is also satisfied through ISP. Intelligent Cloud Broker (ICB) is often used for simplifying the services selection task. ICB acts as an efficient mediator between cloud service provider (CSP) and cloud user. It acts as a tool for provisioning of services. ISP is made possible through the incorporation of machine learning (ML) techniques. Machine learning (ML) technique is widely used in many applications for making the system to produce intelligent decisions.Intelligent service provisioning provides efficient orchestration of services in multi-cloud which involves management of resources, application, and decision-making for provisioning of services. The ISP can perform with the presence of intelligent Broker or without it. This chapter presents an intense study on “Intelligent Service Provisioning (ISP)” which covers the categorization of various service provisioning problems and its solutions, the comparison of various intelligent middleware associated with ISP like broker, the challenges involved in provisioning of services, and the incorporation of various machine learning techniques for efficient provisioning and decision-making. The complete Generic framework for intelligent service provisioning (ISP) for multi-cloud environment is presented. This chapter is organized by providing the overview of services available in single cloud and multi-cloud, the classification of services provisioning and its models and techniques, challenges associated with service provisioning in cloud, state of the art review of the ISP in multi cloud, working principle of machine learning techniques for a case study, and finally the research challenges and future prospects.
Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking

Using physiological data helps to identify the cognitive processing in the human brain. One method of obtaining these behavioral signals is by using eye-tracking technology. Previous cognitive psychology literature shows that readable and difficult-to-read texts are associated with certain eye movement patterns, which has recently encouraged researchers to use these patterns for readability assessment tasks. However, although it seems promising, this research direction has not been explored adequately, particularly for Arabic. The Arabic language is defined by its own rules and has its own characteristics and challenges. There is still a clear gap in determining the potential of using eye-tracking measures to improve Arabic text. Motivated by this, we present a pilot study to explore the extent to which eye-tracking measures enhance Arabic text readability. We collected the eye movements of 41 participants while reading Arabic texts to provide real-time processing of the text; these data were further analyzed and used to build several readability prediction models using different regression algorithms. The findings show an improvement in the readability prediction task, which requires further investigation. To the best of our knowledge, this work is the first study to explore the relationship between Arabic readability and eye movement patterns.
Selective Multi-scale Learning for Object Detection

Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8% improvement in AP (from 39.1% to 40.9%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0% improvement in AP.
Electricity Consumption Forecasting in Iraq with Artificial Neural Network

The goal of this paper is to predict electrical energy consumption using nonlinear autoregressive (NAR) models. The practical section contains historical data on Iraq's annual electricity consumption rate from 1980 to 2013. The most significant findings are that neural networks perform better at predictive analytics due to the hidden layers. To make predictions, linear regression models only use input and output nodes. The hidden layer is also used by the neural network to improve prediction accuracy. This is because it 'learns' in the same way that humans do. It is recommended that further research be undertaken in the following areas Intelligent forecasting methods are being used as an alternative to traditional forecasting methods.
Multi-image Crowd Counting Using Multi-column Convolutional Neural Network

Kurnaz, Oğuzhan Hanilçi, Cemal Crowd density estimation is an important task for security applications. It is a regression problem consisting of feature extraction and estimation steps. In this study, we propose to use a modified version of previously introduced multi-column convolutional neural network (MCNN) approach for estimating crowd density. While in the original MCNN approach the same input image is applied to the each column of the network, we first propose to apply a different version of the same input image to extract a different mapping from each column. Second, original MCNN first generates an estimated density map and then performs crowd counting. Therefore, we adopt it for crowd counting and compare its performance with the proposed method. Regression task is performed by support vector regression (SVR) using feature vectors obtained from MCCNN. 2000 images selected from UCSD pedestrian dataset are used in the experiments. The regions of interest (ROI) are filtered out and the pixel values at the remaining regions are set to zero. In order to prevent distortion caused by camera position, perspective normalization has been applied as a pre-processing step which dramatically improves the performance.
Emotion driven Crisis Response: A benchmark Setup for Multi-lingual Emotion Analysis in Disaster Situations

Emotion analysis from texts has emerged as an important area of research in the field of Natural Language Processing (NLP) in the past few years. Several benchmark datasets have been released for this task, however most of the datasets are open domain in nature and for resource-rich language like English. These datasets may not always be sufficient for capturing domain specific emotions, and may tend to skew towards an emotion based on the domain. In this paper, we provide a framework for multilingual emotion detection to deal with the crisis situations. We collect and annotate disaster domain social media tweets and news data in two languages, namely English and Hindi. We derive 6 emotions from Plutchik's wheel of emotions suitable for disaster domain, and annotate the data using these disaster specific emotions. In total we create four emotionally enriched datasets i.e. 2 tweets datasets (English and Hindi) and 2 news dataset (English and Hindi). We also establish strong baselines on the dataset using two popular deep learning algorithms, stacked Bi-LSTM+CNN and BERT. Evaluation shows that the best model achieves the averaged accuracy of 68% across the four different datasets.
Quantum learning theory: A classical perspective for quantum image

In recent years, enhanced computing power and information accessibility as well as algorithmic advances have resulted to amazing outcomes in taxonomy, information generation, deterioration, reinforcement learning. In spite of these accomplishments, the closeness with the availability limitations of chip production together with the growing volume of data sets is motivate more and more scientists investigate opportunities to use the power of quantum computing to increase the speed of traditional machine learning methods. To examine quantum machine learning works and explore views for a mix of classical machine and quantum computing specialists. The chapter gathers the mainstream quantum image representation, basic quantum algorithms, learning theory and discusses the similarities, differences for some of the quantum image representation. Mostly importance will be given on illuminating the basic of quantum with its image representation, algorithms and learning theory, how they contrast and their best traditional partners and why quantum assets are required to give benefits to learning issues. Practical issues are also discussed; such as how classical information can be uploaded into the quantum.
Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm

With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively predicting the target behavior? The main goal of this article is to use machine learning technology; this article defines the main prediction model by python programming language, analyzes and forecasts the data of previous World Cup, and establishes the analysis and prediction model of football field by K-mean and DPC clustering algorithm. Python programming is used to implement the algorithm. The data of the previous World Cup football matches are selected, and the built model is used for the predictive analysis on the Python platform; the calculation method based on the DPC-K-means algorithm is used to determine the accuracy and probability of the variables through the calculation results, which develops results in specific competitions. Research shows how the machine wins and learns the efficiency of the production process, and the machine learning process, the reliability, and accuracy of the prediction results are improved by more than 55%, which proves that mobile algorithm technology has a high level of predictive analysis on the World Cup football stadium.
Multi-view subspace clustering with consistent and view-specific latent factors and coefficient matrices

Multi-view learning models the relationships between various observations, and is adept to explore the underlying information of data from multiple perspectives. Since well representation is vital for self-expressive subspace clustering, we propose a method called Multi-View Subspace Clustering with Consistent and view-Specific Latent Factors and Coefficient Matrices (MVSC-CSLFCM) that explores the consensus and complementary information of multiple views, and we also impose suitable constraints on coefficient matrices corresponding to the obtained view-specific and consistent representations, respectively. Finally, an effective optimization algorithm based on augmented lagrangian multiplier is introduced to optimize our proposed MVSC-CSLFCM. Comprehensive experiments on four real-world data sets demonstrate the superiority of our proposed method by comparing with a series of state-of-art subspace algorithms.
Gaussian process latent variable model factorization for context-aware recommender systems

Context-aware recommender systems (CARS) have gained increasing attention due to their ability to utilize contextual information. Compared to traditional recommender systems, CARS are, in general, able to generate more accurate recommendations. The latent factors approach accounts for a large proportion of CARS. Recently, a non-linear Gaussian Process (GP) based factorization method was proven to outperform the state-of-the-art methods in CARS. Despite its effectiveness, standard GP model-based methods can suffer from over-fitting and may not be able to determine the impact of each context automatically. In order to address such shortcomings, we propose a Gaussian Process Latent Variable Model Factorization (GPLVMF) method, where we apply an appropriate prior to the original GP model. Our work is primarily inspired by the Gaussian Process Latent Variable Model (GPLVM), which is a non-linear dimensionality reduction method. Since the traditional maximum likelihood approach for standard GP cannot be applied to our model, we adopt a variational inference method to solve the GPLVMF model. As a result, we improve the results on the real datasets significantly as well as capturing the importance of each context. In addition to the general advantages, our method flexibly provides two contributions regarding recommender system settings: (1) addressing the influence of bias by setting a non-zero mean function, and (2) utilizing real-valued contexts by fixing the latent space with real values.
HIR: A Hybrid IR Ranking Model

The aim of information retrieval (IR) process is to respond effectively to user queries by retrieving information that are better meets their expectations. A gap could exist between user information needs and his/her defined context as the level of the user's expertise in the search domain is directly influencing the query richness. The information retrieval system (IRS) must be intelligent enough to identify information needs and respond effectively to meet the expected needs, regardless the level of user's expertise level. This process is difficult and it remains a major and open challenge in the domain of IR. IR models integrate many sources to achieve an effective retrieval system. Semantic IR is an environment in which semantic techniques were applied to sort the documents according to their degree of relevance to the query. The present work proposes a hybrid model to rank documents. The proposed model is based on a query likelihood language model and the semantic similarity between concepts to assess the relevance between query-document pairs. Concepts were extracted by projection on WordNet ontology then word sense disambiguation was conducted. A semantic index was built to validate the proposed model. The conducted empirical experiments show that the proposed model is outperformed the compared benchmarks in the measured IR metrics.