Reviewerly/exHarmony huggingface dataset:
Workflow recognition with structured two-stream convolutional networks

Intelligent monitoring plays an important role in the context of Industry 4.0”, and behavior recognition is one of the research points in computer vision. However, monitoring the workflow of human beings and machines in production process is very difficult in the real-world complex factory environment. In this paper, we propose a novel workflow recognition framework based on the structured two-stream convolutional neural networks (CNNs) to recognize the behavior of both workers and machines. To improve the accuracy of workflow recognition, we use the CNNs to extract the spatial-temporal features and integrate an attention mechanism to detect the valuable behavior. Then, a Video Triple model is introduced to gain extra timestamp information, which can extend the behavior recognition to workflow recognition. Extensive simulation experiments are conducted on THUMOS’14 dataset and a real-world workflow dataset that show the significant performance improvement in video activity recognition.
Performance of Memory Virtualization Using Global Memory Resource Balancing

Virtualization has become a universal generalization layer in contemporary data centers. By multiplexing hardware resources into multiple virtual machines and facilitating several operating systems to run on the same physical platform at the same time, it can effectively decrease power consumption and building size or improve security by isolating virtual machines. In a virtualized system, memory resource supervision acts as a decisive task in achieving high resource employment and performance. Insufficient memory allocation to a virtual machine will degrade its performance drastically. On the contrasting, over allocation reasons ravage of memory resources. In the meantime, a virtual machine's memory stipulates may differ drastically. As a consequence, effective memory resource management calls for a dynamic memory balancer, which, preferably, can alter memory allocation in a timely mode for each virtual machine-based on their present memory stipulate and therefore realize the preeminent memory utilization and the best possible overall performance. Migrating operating system instances across discrete physical hosts is a helpful tool for administrators of data centers and clusters: It permits a clean separation among hardware and software, and make easy fault management. In order to approximate the memory, the stipulate of each virtual machine and to adjudicate probable memory resource disagreement, an extensively planned approach is to build an Least Recently Used based miss ratio curve which provides not only the current working set size but also the correlation between performance and the target memory allocation size. In this paper, the authors initially present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL based Least Recently Used association, dynamic hot set sizing. This assessment outcome confirms that, for the complete SPEC CPU 2006 benchmark set, subsequent to pertaining the 3 optimizing techniques, the mean overhead of MRC construction are lowered from 173% to only 2%. Based on current WSS, the authors then predict its trend in the near future and take different tactics for different forecast results. When there is an adequate amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. These experimental results show that this design achieves 49% center-wide speedup.
Learning a Key-Value Memory Co-Attention Matching Network for Person Re-Identification

Person re-identification (Re-ID) is typically cast as the problem of semantic representation and alignment, which requires precisely discovering and modeling the inherent spatial structure information on person images. Motivated by this observation, we propose a Key-Value Memory Matching Network (KVM-MN) model that consists of key-value memory representation and key-value co-attention matching. The proposed KVM-MN model is capable of building an effective local-position-aware person representation that encodes the spatial feature information in the form of multi-head key-value memory. Furthermore, the proposed KVM-MN model makes use of multi-head co-attention to automatically learn a number of cross-person-matching patterns, resulting in more robust and interpretable matching results. Finally, we build a setwise learning mechanism that implements a more generalized query-to-gallery-image-set learning procedure. Experimental results demonstrate the effectiveness of the proposed model against the state-of-the-art.
Adaptation and validation of the ITC - Sense of Presence Inventory for the Portuguese language

This investigation concerns the translation and validation of the ITC - Sense of Presence Inventory (ITC-SOPI) for the Portuguese-speaking population (in Europe), estimating the validity of the content and concepts and the maintenance of an equivalent semantics. It also sought to verify its psychometric properties, namely its factorial validity and internal consistency. The sample consisted of 459 individuals, 274 males and 185 females. The fidelity of the subscales varied between 0.67 and 0.89. Confirmatory factor analysis revealed a theoretical model of 35 items, divided by four factors. After fixing some of the residual errors between items, the following adjustment indexes were calculated: χ2/df = 2.301; goodness fit index = 0.860; comparative fitness index = 0.889; root mean square error of approximation = 0.053; Akaike’s information criterion = 1420. Based on the observed results and the robustness of the sample size used, the obtained theoretical model shows that the ITC-SOPI is recommended to measure presence in virtual reality research projects with samples of Portuguese language speakers.
Chinese NER with Height-Limited Constituent Parsing

In this paper, we investigate how to improve Chinese named entity recognition (NER) by jointly modeling NER and constituent parsing, in the framework of neural conditional random fields (CRF). We reformulate the parsing task to heightlimited constituent parsing, by which the computational complexity can be significantly reduced, and the majority of phrase-level grammars are retained. Specifically, an unified model of neural semi-CRF and neural tree-CRF is proposed, which simultaneously conducts word segmentation, part-ofspeech (POS) tagging, NER, and parsing. The challenge comes from how to train and infer the joint model, which has not been solved previously. We design a dynamic programming algorithm for both training and inference, whose complexity is O(n·4h), where n is the sentence length and h is the height limit. In addition, we derive a pruning algorithm for the joint model, which further prunes 99.9% of the search space with 2% loss of the ground truth data. Experimental results on the OntoNotes 4.0 dataset have demonstrated that the proposed model outperforms the state-of-the-art method by 2.79 points in the F1-measure.
Initialized Equilibrium Propagation for Backprop-Free Training

Deep neural networks are almost universally trained with reverse-mode automatic differentiation (a.k.a. backpropagation). Biological networks, on the other hand, appear to lack any mechanism for sending gradients back to their input neurons, and thus cannot be learning in this way. In response to this, Scellier & Bengio (2017) proposed Equilibrium Propagation - a method for gradient-based train- ing of neural networks which uses only local learning rules and, crucially, does not rely on neurons having a mechanism for back-propagating an error gradient. Equilibrium propagation, however, has a major practical limitation: inference involves doing an iterative optimization of neural activations to find a fixed-point, and the number of steps required to closely approximate this fixed point scales poorly with the depth of the network. In response to this problem, we propose Initialized Equilibrium Propagation, which trains a feedforward network to initialize the iterative inference procedure for Equilibrium propagation. This feed-forward network learns to approximate the state of the fixed-point using a local learning rule. After training, we can simply use this initializing network for inference, resulting in a learned feedforward network. Our experiments show that this network appears to work as well or better than the original version of Equilibrium propagation. This shows how we might go about training deep networks without using backpropagation.
MA-Shape: Modality Adaptation Shape Regression for Left Ventricle Segmentation on Mixed MR and CT Images

Left ventricle (LV) segmentation is essential to clinical quantification and diagnosis of cardiac images. While most existing LV segmentation methods focus on cardiac images of single modality or multi-modality, few have been devoted to images of mixed-modality. By Mixed-Modality, we mean that different modalities exist in the database, while for every subject, there is only one modality. In this paper, we propose a newly invented LV segmentation method from mixed-modality images: modality adaptation shape regression (MA-Shape). Compared to single-modality or multi-modality methods, the proposed MA-Shape can 1) be applied to images of new modality during the test phase, which improves the generalization of the learned methods, and 2) take advantage of existing samples of different modalities, which alleviates the high demand for multi-modality data. To achieve this, we propose a modality adaptation module to enhance the shape consistency between the MR and CT, and therefore improve the generalization of the model learned in one modality to new modalities. The experiments on a dataset with MR sequences of 145 subjects and CT scans of 96 subjects validate that the proposed MA-shape can achieve excellent performance by learning common shape information from images of mixed modality and improve the cross-modality generalization of shape regression model learned on images of one modality. These advantages not only provide an efficient way of utilizing mix-modalities data during model learning but also enables an effective and flexible way of applying automated cardiac function assessment in clinical practice.
K-means and bayesian networks to determine building damage levels

Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of continuous and discrete variables. Therefore, to simplify the study it is assumed that all variables are discrete in order to solve practical problems in the implementation of theory. Discretization method used is the K-Means clustering because the percentage of validity obtained by this method is greater than the binning method.
Five Ethical Challenges of Immersive Journalism: A Proposal of Good Practices’ Indicators

Immersive Journalism brings new ethical challenges because some practices and procedures go against conventional journalistic standards. Therefore, the aim of this study was precisely to question decisions and choices taken by professional journalists when they produce and edit immersive content like 360º video reports or virtual reality reconstructions of news. As a result, five main ethical challenges of Immersive Journalism have been identified: image integrity; reconstruction of news; sources and staging; role of the journalist and/or recording team; and sensitive content. Each of these is accompanied by a proposal of good practices to deal with these challenges and to promote reflection among professionals.
Diagnostic Accuracy of Breast Medical Tactile Examiners (MTEs): A Prospective Pilot Study

The usefulness of clinical breast examination (CBE) in general and in breast cancer screening programs has been a matter of debate. This study investigated whether adding vision-impaired medical tactile examiners (MTEs) improves the predictiveness of CBE for suspicious lesions and analyzed the feasibility and acceptability of this approach.The prospective study included 104 patients. Physicians and MTEs performed CBEs, and mammography and ultrasound results were used as the gold standard. Sensitivity and specificity were calculated and logistic regression models were used to compare the predictive value of CBE by physicians alone, MTEs alone, and physicians and MTEs combined.For CBEs by physicians alone, MTEs alone, and both combined, sensitivity was 71, 82, and 89% and specificity was 55, 45, and 35%, respectively. Using adjusted logistic regression models, the validated areas under the curve were 0.685, 0.692, and 0.710 (median bootstrapped p value (DeLong) = 0.381).The predictive value for a suspicious breast lesion in CBEs performed by MTEs in patients without prior surgery was similar to that of physician-conducted CBEs. Including MTEs in the CBE procedure in breast units thus appears feasible and could be a way of utilizing their skills.
A New View of the Nature of Reality and the Teaching Higher-Level Cognitive Strategies

The authors state the transition of the modern materialistic worldview paradigm to the idealistic paradigm.The transition to a new view of the nature of reality essentially turns modern education to the defining meanings of Plato's Paideia.The authors reinterpret greek paideia taking into account the current knowledge in science and philosophy.The methods used by the authors have complex crossdisciplinary character.They combine the following basic strategies: the history of ideas, analytics, comparativistics, and phenomenology.The authors conclude that the role of the teacher is changing in the Mental Universe Model or the "Evolving Matter," which forms the basis of an idealistic paradigm.The teacher turns into an important actor of a student's brain neuroprogramming, who is directly responsible for the quality of his work.The authors proposed three basic higher-level cognitive strategies by means of which the teacher embodies the reinterpreted and modernized achievements of the greek paideia.The interrelationship between a teacher and a student gains harmony and provides a joint ascent to the image of a higher idea or to universal consciousness if: 1) a teacher does not provide knowledge, but sets tasks and solves them with a student.2) the full value of a student's selfrealization depends directly on the usefulness of a teacher's self-realization.3) choosing a teacher, a student chooses his way of life.
APLIKASI METODE ARIMA BOX-JENKINS UNTUK MERAMALKAN KASUS DBD DI PROVINSI JAWA TIMUR

The Box-Jenkins forecasting method is one of the time series forecasting methods. This method uses past values as dependent variables and independent variables are ignored. Box-Jenkins (ARIMA) method has advantages that can be used on non-stationary data, can be used on all data patterns including seasonal data patterns so this method can be used to predict cases of DHF in East Java Province. This research was conducted to determine the best model with seasonal ARIMA forecasting model and also to analyze the result of DHF case forecasting in East Java Province. The analysis result shows that the best model for DHF case in East Java Province is ARIMA (1,1,2)(2,1,1)12. The best model has fulfilled the test requirement that is parameter significance test and diagnostics check. Forecasting results show the number of DHF cases in 2017-2018 will experience an upward trend. The total number of DHF cases in 2017 was 14,277 cases and increased to 22,284.54 DHF cases in 2018. The forecasting results showed that the highest peak of DHF cases occurred in January 2017 with 1,914.22 cases and then decrease in the next month until the lowest case occurred in October with 768.46. The forecast for 2018 also shows that the highest DHF cases occurred in January with 3455.55 and declined to the lowest in October with 1126.49 cases. MAPE value in the forecast is 43.51%. The MAPE value indicates that the forecasting is good enough, adequate and feasible to use.
A Global Method for Non-Rigid Registration of Cell Nuclei in Live Cell Time-Lapse Images

Non-rigid registration of cell nuclei in time-lapse microscopy images can be achieved through estimating the deformation fields using optical flow methods. In contrast to local optical flow models employed in the existing non-rigid registration methods, we introduce approaches based on a global optical flow model. Our registration model consists of a data fidelity term and a regularization term. We compared different regularizers for the deformation fields and found that a convex quadratic function is more suitable than non-convex ones. To improve the robustness, we propose an adaptive weighting scheme based on the statistics of the noise in fluorescence microscopy images as well as a combined local-global scheme. Moreover, we extend the global method by exploiting high-order image features. The best suitable high-order features are determined through learning two generative image models, namely, fields of experts and convolutional Gaussian restricted Boltzmann machine, whose model formulations are both consistent with the assumption of high-order feature constancy in the registration model. Using multiple data sets of real 2D and 3D live cell microscopy image sequences as well as synthetic image data, we demonstrate that our proposed approach outperforms the previous methods in terms of both registration accuracy and computational efficiency.
A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples

Building on insights from the differential privacy literature, we develop a simple noise-infusion method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on small samples. Although our method does not offer a formal privacy guarantee, it outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by census tract in the Opportunity Atlas. We provide a step-by-step guide and code to implement our approach.
An AutomationML Based Ontology for Sensor Fusion in Industrial Plants

AutomationML (AML) can be seen as a partial knowledge-based solution for manufacturing and automation domains since it permits integrating different engineering data format, and also contains information about physical and logical structures of production systems, using basic concepts as resources, process, and products, in semantic structures. However, it is not a complete knowledge-based solution because it does not have mechanisms for querying and reasoning procedures, which are basic functions for semantic inferences. Additionally, AutomationML does not deal with aspects of sensor fusion naturally. In this sense, we propose an ontology to describe those sensors’ fusion elements, including procedures for runtime processing, and also elements that can turn AutomationML into a complete knowledge-based solution. The approach was applied in a case study with two different industrial processes with some sensors under fusion. The results obtained demonstrate that the ontology allows describing sensors that are under fusion and deal with the occurrence of data divergence. In a broader view, the results show how to apply AutomationML description for runtime processing of data generated from different sensors of a manufacturing system using an ontology to complement the AML description, where AutomationML concentrates knowledge about a specific production system and the ontology describes a general and reusable knowledge about sensor fusion.
An Improved Particle Filtering Algorithm Using Different Correlation Coefficients for Nonlinear System State Estimation

Particle filtering (PF) algorithm has found an increasingly wide utilization in many fields at present, especially in nonlinear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. This article proposed an improved PF algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in Matlab, it discovers that the proposed algorithm provides better accuracy than sequential importance resampling, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filters in Gaussian mixture noise.
ENERGY AND EXERGY ANALYSIS OF A SOLAR PHOTOVOLTAIC PERFORMANCE IN BAGHDAD

Photovoltaic modules usually generate electricity from a specific range of light frequencies and cannot cover the whole solar range of infrared, ultraviolet and diffused light. Hence, much of the striking sunlight energy is wasted by the solar modules. Thus, energy and exergy analysis were conducted to determine the performance of a solar photovoltaic module in Baghdad, Iraq. An engineering equation solver (EES) software has been using to develop the mathematical model. The environmental parameter of solar radiation, ambient temperature, and wind speed were obtained using Meteonorm software. The operating parameters of a PV module includes normal operation cell temperature, open-circuit voltage, and short-circuit current were obtain from manufacturer data sheet. The results showed that, the exergy efficiency ranged from 10.8% to 15.8 %, while the energy efficiency varies between 15.71% to 15.74 % and the exergy destruction varied from 182.8 to 352.3 W/m2 throughout the year. It has been found that, the first law efficiency was greater than second law efficiency. The differences between the two efficiencies from January to December are (25.6%, 31.1%, 25.1%, 25.6%, 17.8%, 9.6%, 9.6%, 1.2%, 0.5%, 0.45%, 2.5%, and 14.6%) respectively. While the exergy destruction through the same 12- months are (195.4, 233.5, 304.3, 352.3, 333.8, 292.9, 309.3, 274.6, 249.8, 215.9, 187.4, and 182.8) W/m2.
Defunding Hate: PayPal’s Regulation of Hate Groups

The riot by white supremacists in Charlottesville, Virginia, in August 2017, generated a public debate about the role of platforms in policing users involved in violent hate speech. PayPal’s efforts on this issue, in removing services from some designated hate groups while continuing to serve others, highlights the challenges payment platforms face when they act, whether formally or informally, as regulators. This article examines PayPal’s policies and enforcement efforts, both proactive and reactive, in removing its services from hate groups in the United States. It pays particular attention to the surveillance and screening practices that PayPal employs to proactively detect users who violate its policies. The article argues that public calls for PayPal to identify and remove its services from hate groups raise critical questions about ceding broad regulatory authority to platforms and reveal the serious challenges of relying upon commercial enterprises to address complex social problems.