Reviewerly/exHarmony huggingface dataset:
FONDUE: Framework for Node Disambiguation Using Network Embeddings

Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest form, networks represent real-life entities (e.g. people, papers, proteins, concepts) as nodes, and describe them in terms of their relations with other entities by means of edges between these nodes. This can be valuable for a range of purposes from the study of information diffusion to bibliographic analysis, bioinformatics research, and question-answering. The quality of networks is often problematic though, affecting downstream tasks. This paper focuses on the common problem where a node in the network in fact corresponds to multiple real-life entities. In particular, we introduce FONDUE, an algorithm based on network embedding for node disambiguation. Given a network, FONDUE identifies nodes that correspond to multiple entities, for subsequent splitting. Extensive experiments on fourteen benchmark datasets demonstrate that FONDUE is substantially and uniformly more accurate for ambiguous node identification compared to the existing state-of-the-art with a lower computational cost, while less optimal for determining the best way to split ambiguous nodes.
Efficient Reservoir Computing using FPGA and Electro-optic Modulation

We experimentally demonstrate a reservoir computing system consisting of an electro-optic modulator and FPGA electronics. On the NARMA10 benchmark test, the normalized root mean square error can achieve as low as 0.134.
Bot mitigation – how gapsin understanding and ownership are exposingbusinesses to greater threats

When people think of security, there is a tendency to think of building high walls around what needs to be protected. Thieves are, after all, opportunists, and if a target can look too tricky to breach then they will look elsewhere for an easier score. This is no longer true of cyber security. The poor security of other businesses is no longer your advantage – it's now your problem. When people think of security, there is a tendency to think of building walls around what needs to be protected. Thieves are opportunists, and if a target looks too tricky, they will go elsewhere. But this is no longer true of cyber security. The poor security of other businesses is no longer to your advantage – it's now your problem. Using credentials stolen elsewhere, bots are probing your defences looking for weaknesses such as password reuse. Without a deep understanding of how bots operate, companies are putting themselves at risk, argues Andy Still of Netacea.
Sequential View Synthesis with Transformer

This paper addresses the problem of novel view synthesis by means of neural rendering, where we are interested in predicting the novel view at an arbitrary camera pose based on a given set of input images from other viewpoints. Using the known query pose and input poses, we create an ordered set of observations that leads to the target view. Thus, the problem of single novel view synthesis is reformulated as a sequential view prediction task. In this paper, the proposed Transformer-based Generative Query Network (T-GQN) extends the neural-rendering methods by adding two new concepts. First, we use multi-view attention learning between context images to obtain multiple implicit scene representations. Second, we introduce a sequential rendering decoder to predict an image sequence, including the target view, based on the learned representations. Finally, we evaluate our model on various challenging datasets and demonstrate that our model not only gives consistent predictions but also doesn’t require any retraining for finetuning.
Hybrid Independent Learning in Cooperative Markov Games

Independent agents learning by reinforcement must overcome several difficulties, including non-stationarity, miscoordination, and relative overgeneralization. An independent learner may receive different rewards for the same state and action at different time steps, depending on the actions of the other agents in that state. Existing multi-agent learning methods try to overcome these issues by using various techniques, such as hysteresis or leniency. However, they all use the latest reward signal to update the Q function. Instead, we propose to keep track of the rewards received for each state-action pair, and use a hybrid approach for updating the Q values: the agents initially adopt an optimistic disposition by using the maximum reward observed, and then transform into average reward learners. We show both analytically and empirically that this technique can improve the convergence and stability of the learning, and is able to deal robustly with overgeneralization, miscoordination, and high degree of stochasticity in the reward and transition functions. Our method outperforms state-of-the-art multi-agent learning algorithms across a spectrum of stochastic and partially observable games, while requiring little parameter tuning.
A Portfolio-Driven Development Model and Its Management Method of Agile Product Line Engineering Applied to Automotive Software Development

In recent automotive systems development, realizing both variability and agility is the key competitiveness to meet the diverse requirements in global markets and rapidly increasing intelligent functions. This article proposes a portfolio-driven development method and its management method of APLE (Agile Product Line Engineering). The proposed method is intended to manage agile evolution of multiple product lines while increasing variability of products. To establish a portfolio management of development resources, it is necessary for an organization to manage multiple product lines on APLE in an entire development. We propose a portfolio-driven development method of three layers on APLE and its management method based on a concept of portfolio management life cycle. We applied the proposed management model and method to the multiple product lines of automotive software systems, and demonstrated an improvement of manageability with better predictability of both productivity and development size. This article contributes to provide an entire development management method for APLE, and its practical experience in the automotive multiple product lines.
Target points for professional educational organizations development in territories with special organization of production and residence

The article defines the target points for the secondary vocational education organizations, located in territories that simultaneously have the status of “single-industry city”, “closed administrative-territorial unit” and “advanced development zone” development (the cities of Seversk, Ozersk, Snezhinsk, Zheleznogorsk, Zarechny). Based on information and analytical materials for the training quality monitoring, vectors allowing these institutions to achieve high indicators of both “The national project “Education”“ and the territorial development indicators values have been formed. The main designated target points are global trends, regional projects within the framework of Federal projects, priority economic activities in these territories, and the interests of city – forming enterprises.
SENTEI: Filter-Wise Pruning with Distillation towards Efficient Sparse Convolutional Neural Network Accelerators

In the realization of convolutional neural networks (CNNs) in resource-constrained embedded hardware, the memory footprint of weights is one of the primary problems. Pruning techniques are often used to reduce the number of weights. However, the distribution of nonzero weights is highly skewed, which makes it more difficult to utilize the underlying parallelism. To address this problem, we present SENTEI*, filter-wise pruning with distillation, to realize hardware-aware network architecture with comparable accuracy. The filter-wise pruning eliminates weights such that each filter has the same number of nonzero weights, and retraining with distillation retains the accuracy. Further, we develop a zero-weight skipping inter-layer pipelined accelerator on an FPGA. The equalization enables inter-filter parallelism, where a processing block for a layer executes filters concurrently with straightforward architecture. Our evaluation of semantic-segmentation tasks indicates that the resulting mIoU only decreased by 0.4 points. Additionally, the speedup and power efficiency of our FPGA implementation were 33.2× and 87.9× higher than those of the mobile GPU. Therefore, our technique realizes hardware-aware network with comparable accuracy.
Domain-Transferred Face Augmentation Network

The performance of a convolutional neural network (CNN) based face recognition model largely relies on the richness of labelled training data. However, it is expensive to collect a training set with large variations of a face identity under different poses and illumination changes, so the diversity of within-class face images becomes a critical issue in practice. In this paper, we propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) that can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets of other domains. Extending from StarGAN’s architecture, DotFAN integrates with two additional subnetworks, i.e., face expert model (FEM) and face shape regressor (FSR), for latent facial code control. While FSR aims to extract face attributes, FEM is designed to capture a face identity. With their aid, DotFAN can separately learn facial feature codes and effectively generate face images of various facial attributes while keeping the identity of augmented faces unaltered. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity so that a better face recognition model can be learned from the augmented dataset.
Taxonomy of keyword extraction in Facebook using Decision Tree algorithm in NLP

The main idea of our project is to extract keywords from the collection of dataset from Facebook account data like comment, post by the people. Then, By extracting the keywords from the specific account, we can provide the advertisement with help of the business organizations, to improve the business growth of each organization. Text can be an extremely valuable source of information, but extracting insights from the data can be hard and time-consuming due to its unstructured nature. Businesses are performing to text classification for structuring text in a fast and cost-efficient way to enhance decision-making and automate processes in the model. Instead of relying on manually crafted rules, text classification in machine learning learns to make classifications based on past observations. By using pre-labelled examples as training data, a machine learning algorithm can learn the different subset between pieces of text and that a particular output is expected for a particular input.
Spatial Class Distribution Shift in Unsupervised Domain Adaptation: Local Alignment Comes to Rescue

We propose a method for semantic segmentation in the unsupervised domain adaptation (UDA) setting. We particularly examine the domain gap between spatial-class distributions and propose to align the local distributions of the segmentation predictions. Despite its simplicity, the proposed method achieves state-of-the-art results in UDA segmentation benchmarks.
A Proficient Scheme for Detecting Breast Cancer by Classification Techniques

Cancer is a risky disease which could affect the particular area in depth and may risk the body parts. Now a days, more females are subject to breast cancers. So that Machine Learning Techniques has proposed to analyze the risky area in which the information is utilized for forecasting additional incidents. Machine Learning is popular scheme within several programs one remaining healthcare evaluation. Image Classification as well as feature extraction will bring the affected area’s image into several analyzing methods. With this proposed system, we’ve suggested an CNN (Convolution Neural Network) active design which fetches a sequence of pictures coming from a healthcare scanner repository so that the pictures are preprocessed as well as additional segmented feature extraction. The effectiveness on the suggested design is examined and it is as opposed along with other Machine Learning procedures and it is found the proposed system has supplies the greater results. The functionality on the unit tends to be more precise as the unit has an iterative method for include removal inside classifying pictures. There are some images are kept for the training and testing. We have achieved the accuracy level of comparing with existing model.
Decentralized Privacy Protection Framework Based on Attribute-Based Signcryption

In the last decade, the digital world has widely changed how we handle our daily actions on the internet. India has the second-largest internet users in the globe with about 370 million users. The Indian business market has understood the significance of the threats in managing digital data. This has led to the increased allocation of budgets for the implementation of security to protect data from breaches. The data security has been constantly improvised and the existing systems include various schemes focusing on central authority which must be fully trusted and is responsible for both attribute management and key generation. This system introduces multi-authority using cipher-text instead of key-policy ABSC scheme without destroying the attribute security. Since, the mechanism to verify does not require plaintext. It accepts any Monotone Boolean Function. Finally,a partial decryption algorithm is used to outsource operations to the cloud to save computational resources.
Arabic Named Entity Recognition on Social Media based on feature selection techniques usi ng SVM-RFE

With the massive expansion of information in social media, a high demand exists for information retrieval techniques. The recognition of the named entity (NE) such as person, location, organization, etc. has emerged as one of the main tasks in natural language processing. Often, utilizing the entire feature set may not only be time-consuming but may also have a negative effect on performance. Due to the high number of features, it is difficult to identify the subset of features relevant to a given task. In this paper, we apply feature selection methods based on the support vector machine recursive feature elimination (SVM-RFE) to find the optimized feature set. Afterward, an optimized feature set combination is used to identify and classify named entities (NEs) based on the Support Vector Machine (SVM). The proposed method is evaluated using Darwish's dataset (a publicly available benchmark for Arabic NER for social media). Experimental results demonstrate the effectiveness of feature selection in enhancing performance and outperform the most advanced systems.
Network planning methodology in the problem of adaptive learning of university students

The article proposes a methodology of network planning in the problem of adaptive learning of university students. The elements of network planning are briefly presented, the use of adaptive learning methods in the educational process is described. The learning process in the university is considered, and the possibilities of describing it using the formalism of network planning within some discipline are analyzed. The developed technique for assessing the performance of tasks is given. The process of interaction between a teacher and a student in the learning process is described in terms of assessing the sequence of this interaction and the parameters of the proposed algorithm. The developed model of the discipline in the form of a linear sequence of actions for the study of the discipline is presented. To reflect the efforts made to obtain a learning outcome, a graphical representation of the discipline's network model is given. The results of the use of network graphics in the educational process within one discipline are presented. The formulas used to assess the student when using this technique are described. Several variants of passing the academic discipline by a student are considered, analytical data obtained by comparing these variants are presented. A computer program designed to support the developed technique is presented. The results of using the proposed methodology for assessing the study of a discipline by a university student are analyzed. The study of the methodology presented in the article can be useful for both university teachers and students.
Image And Text Encrypted Data With Authorized Deduplication In Cloud

In this paper, the role re-encryption is used to avoid the privacy data lekage and also to avoid the deduplication in a secure role re-encryption system(SRRS). And also it checks for the proof of ownership for to identify whether the user is authorized user or not. This is for the efficiency. Role re-encrytion method is to share the access key for the corresponding authorized user for accessing the particular file without the leakage of privacy data. In our project we are using both the avoidance of text and digital images. For example we have the personal images in our mobile, handheld devices, and in the desktop etc., So, as these images have to keep secure and so we are using the encryption for to increase the high security. The text file also important for the users now-a-days. It has to keep secure in a cloud server. Digital images have to be protected over the communication, however generally personal identification details like copies of pan card, Passport, ATM, etc., to store on one's own pc. So, we are protecting the text file and image data for avoiding the duplication in our proposed system.
Cybersecurity Risks in Romanian Companies

During the last period, information and communication systems became an essential component of our society that, at its turn, became more and more dependent on these infrastructures. At the same time, these systems are undergoing a convergence and interconnection process which, besides its benefits come to raise specific threats to their users' interests. Due to the high level of interconnectivity, it is essential that all members of the cyber community to be aware of their responsibilities in information security. Rising awareness is the first step towards a more trustful and credible communication information environment that is a pre-requisite of the information society. This chapter demonstrates, on the one hand, how Romanian organizations are able to optimize their information security risk in order to streamline their activities and, on the other hand, how security mechanisms have to be properly designed and commensurate with the specific threats for the specific types of information.
Cross- Scenario Person Re-identification

Abstract Person Reid is a challenging task for two factors, first one is background interference, such as changes in light, weather, posture, and camera position. Second is domain adaptive capacity, such as model train by market1501 achieve the same performance on Duke dataset. To solve the above problems, we come up with adopt human semantic to remove clutter from unwanted background information, is naturally a better alternative compare with bounding box, we adopt Local Regions Representation to extra the image features, which can preeminently improve the representation of local feature and global feature. Our proposed CSReID integrates human semantic and Local Regions Representation in person re-identification and not need to train on the evaluation dataset can achieve state of the art cross-modal performance.
An Introductory Study on Image Quality of Dehazed Images

In real World conditions, e.g., fog and haze, the surrounding environment contains a large number of microparticles. This creates a medium which interferes with propagating light, changing the appearance of objects within it. When a digital image is captured in this type of medium, the image will be hazy due to blurring and loss of detail. This is a problem for many automated computer vision methods which utilize visual cues in the image. Multiple methods have been proposed to perform image dehazing, but it is difficult to standardize the perceived quality of the estimated clear image. A highly accurate estimation is not always possible because the nature of the problem is m-posed and an exact clear ground truth is usually unavailable. In fact, the goal of many dehazing methods is to simply obtain a visually pleasing understandable image. In this paper, the Pix2pix image to image translation network and the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement method are used to dehaze hazy images. The resulting clear images will be subject to both subjective and objective image quality assessments. Lastly, this paper will also provide an analysis to discuss the important factors in the attempt to obtain a better dehazed image.
Predicting Mineral Sites through Machine Learning Model of Buffer Zone Geophysical and Geochemical Trends

The presence of mineral sites could be predicted using a model based on logistic regression. The model is built from geophysical and geochemical property data trends of the buffer zones (areas) around the proven ground truth gold (Au) versus iron (Fe) sites across Australia as the case. The trends summarized within the zones are the mean, minimum value, maximum value, and range of the aforementioned geophysical and geochemical properties. Principal Component Analysis (PCA) is used to increase the quality of data columns (bands) used in building the model, while decreasing the number of columns used. Logistic regression is used in predicting the presence of Au versus iron in individual sites, based on available data on them given by the PCA bands. The plot of PCA bands display diverging directions of trends between Au and Fe sites, while the resulting predictive model possesses an accuracy of 94%.
PI and Fuzzy Controllers for Non-Linear Systems: A Case Study

This work presents the development and the study of a nonlinear system structure capable of moving in one degree of freedom. The motion is performed by a brushless motor coupled to a propeller. For this, the mathematical modeling of the developed structure was based on resembling a simple physical pendulum. Regarding the system control, two techniques were considered: PID and Fuzzy, implemented in Matlab. Simulation tests were carried out to validate and compare the control technique results. The structure developed can serve as a base platform for future improvements, studies, and experiment tests, such as the analysis of motion in the horizontal plane.
Semiautomatic Annotation Framework Based on Annotator Habit for Digital Breast Tomosynthesis

Annotation is an essential task before training a model of classifier in machine learning. In general, people use a photo editor to draw contours represented as annotated results. However, most photo editors are limited to decode medical images with specified formats, such as Digital Imaging and Communication in Medicine (DICOM). In this paper, we propose a semiautomatic annotation framework for digital breast tomosynthesis (DBT). The challenge is to annotate multiple images of DBT simultaneously and keep annotation consistent within the adjacent images. First, we analyze the manual-annotated results in mammograms. Then, the proposed framework is established by referring to the analytics of manual annotation, which is composed of three phases, namely lesion pre-detection, extendable annotation, and key-points generation. Finally, an annotation tool is developed based on the proposed framework. The experiment results demonstrate that our tool is capable of achieving simultaneous annotation and result consistency to multiple images in DBT.