Reviewerly/exHarmony huggingface dataset:
Remove to Improve?

The workhorses of CNNs are its filters, located at different layers and tuned to different features. Their responses are combined using weights obtained via network training. Training is aimed at optimal results for the entire training data, e.g., highest average classification accuracy. In this paper, we are interested in extending the current understanding of the roles played by the filters, their mutual interactions, and their relationship to classification accuracy. This is motivated by observations that the classification accuracy for some classes increases, instead of decreasing when some filters are pruned from a CNN. We are interested in experimentally addressing the following question: Under what conditions does filter pruning increase classification accuracy? We show that improvement of classification accuracy occurs for certain classes. These classes are placed during learning into a space (spanned by filter usage) populated with semantically related neighbors. The neighborhood structure of such classes is however sparse enough so that during pruning, the resulting compression bringing all classes together brings sample data closer together and thus increases the accuracy of classification.
On the Security of Permissionless Blockchain Systems: Challenges and Research Perspective

Blockchain technology enables global mutually trustless participants to reach a consensus on the final state of permissionless distributed and decentralized ledgers. Due to its properties of openness, transparency, irreversibility, and credibility, many systems have been built based on blockchains' structure, such as Bitcoin and Ethereum. With the wide application of blockchains, however, many security problems still exist in blockchains and there have been many malicious attacks against blockchain systems. Although these attacks have been proposed, there lacks a systematic exploration of how these attacks have been conducted and what underlying relationship they have. In this paper, we firstly present and summarize several current attacks on blockchains in three aspects: system deficiency attacks, mining attacks, and network-level attacks. Secondly, we conduct a systematic analysis and evaluation of the possibility of these attacks occurring on major blockchain platforms. Finally, we motivate some research perspectives and challenges for blockchain system security and highlight some potential solutions to these problems.
A Subsequent Words Recommendation Scheme for Chinese Input Method based on Deep Reinforcement Learning

In order to improve the accuracy of subsequent word recommendation for Chinese input methods and increase users' input speed, we propose a recommendation scheme for subsequent word input based on deep reinforcement learning and build a model for this scheme. In this paper, we train the model based on the deep Q-network (DQN) procedure. Firstly, the state feature extraction module is used to obtain state features with different lengths of historical input information. Secondly, the users are required to select the target items from the recommendation list generated by the word prediction module. Then, the model feeds back and updates the status of the user's selection according to the reward and punishment rules designed in this paper. Finally, through continuous human-computer interaction, the model is optimized and adjusted to obtain the optimal strategy. The experiments show that the proposed scheme makes a certain improvement in the accuracy of subsequent word recommendation compared with traditional methods.
Revealing Coordination Needs from Task Allocation for Team Performance Estimation

In scrum, the development team is responsible for converting the selected product backlog items of a sprint to a "done" product increment. In sprint planning, the team develops product functionality to fulfill the selected product backlog items. In order to implement the product functionality, the team plans needed activities and then decomposes the activities into tasks. When dependent tasks are assigned to different developers, there is need for coordination between developers. The needs of coordination are difficult to quantify, however, quantification of the needs of coordination is an important factor to accurately estimate team performance of a sprint. In this paper, a method is proposed to calculate distribution of task dependency among developers, and the distribution is defined as Coordination Needs. An experiment on an open source project, Spring, demonstrates that the Coordination Needs is strongly correlated with team performance. With the method, the Coordination Needs of task allocation is revealed so team performance of a sprint can be accurately estimated, and task allocation can be optimized.
Multi-keyword Cipher-text Retrieval Method for Smart Grid Edge Computing*

Abstract With the continuous deepening of China's power grid intelligent upgrading and the rise of edge computing technology, power grid terminals can carry out data storage and intelligent calculation at the edge of the power grid. However, the generated data by users may cause the leakage of user privacy information. The power grid edge system can retrieve the user's electricity data on demand, so as to improve the level of privacy protection and reduce the demand of transmission and computing resources through the localized storage of data. Therefore, how to carry out privacy protection and on-demand retrieval is a key problem to realize the intelligent power grid edge. In this paper, a multi-keyword cipher-text retrieval scheme (MCR- SGEC) for smart grid edge computing is proposed, which can achieve accurate matching of index and trap door, and has low spatial complexity. At the same time, on this basis, a multi-keyword fuzzy cipher-text retrieval method for smart grid edge computing (MFSE-SGEC) is proposed, which allows users to have spelling errors, which makes the user experience better.
A Practical Privacy-Preserving Algorithm for Document Data

A huge number of documents such as news articles, public reports, and personal essays has been released on websites and social media. Once documents including privacy-sensitive information are published, the risk of privacy breaches increases; thus, documents should be carefully checked before publication. In many cases, human experts redact or sanitize documents before publishing; however, this approach is sometimes inefficient with regard to its cost and accuracy. Furthermore, critical privacy risks may remain in the documents. In this paper, we present a generalized adversary model and apply it to document data. This paper devises an attack algorithm for documents, which uses a web search engine, and proposes a privacy-preserving algorithm against the attacks. We evaluate the privacy risks for real accident reports from schools and court documents. As experiments using the real reports, we show that human-sanitized documents still include privacy risks, and our proposal would contribute to risk reduction.
Research on Encrypted Text Classification Based on Natural Language Processing

Abstract In reality, data encryption technology is mostly used to protect the security of text data in the network, but when we need to obtain these data, this layer of encryption becomes an obstruction to obtaining data. The general method uses data mining and data decryption to extract effective information. The experimental data in this article selected 20 categories of text information, and obtained a data set with a difficulty of 1 to classify the encrypted text information. In order to classify encrypted text more effectively, this paper studies the method of using the logistic regression model and the LightGBM model algorithm to directly process encrypted text, which can directly extract and classify the text in the encrypted state. Model evaluation results show that LightGBM is more effective. In addition, this article provides a basic framework for the classification of encrypted text based on natural language processing.
Data deduplication for cloud storage

As huge amounts of data are produced and stored for various applications, then the problem arises in data storage as well as access time of the data. The stored data are accessed in the least amount of time, despite there exists duplicates of data in the storage. This becomes easier by suing cloud storage devices due to easy access of data and their duplicates. Memory reutilization makes the storage efficient. The data can be backed up in the cloud and restored easily when there is crash preventing inconsistency in data. For deduplication, logical pointers and the hash functions are used along with deduplication manager that replaces the duplicate data with pointers and keeps track of duplicates irrespective of the load. The security of the data is also a key factor along with data consistency. This chapter discusses data deduplication for cloud computing and explains how the cloud helps in addressing the issues related to duplicates of data.
CURRENT TASKS OF EDUCATION DEVELOPMENT FOR SUSTAINABLE DEVELOPMENT OF PEDAGOGICAL SCIENCES

This article shows the analysis of educational development of current pedagogical sciences in a sustainable way. Methods of improving and increasing efficiency pedagogical scientific research has been examined. Sustainable educational development is a new, innovative way of changing system that avoids application of current methods, technologies. In this sustainable search, there are couple of questions that needs to be addressed: «What to teach, how to teach, and why to teach?». In order to find answers, theoretical background is required.
Numerical Simulation System of Aerocraft Aerodynamic Characteristics for Intelligent Internet of Things

In the current numerical simulation system of aircraft aerodynamic characteristics, the calculation of motion coordinates is not good. When the data deviation occurs, the numerical deviation cannot be effectively controlled, which affects the simulation accuracy. Therefore, a numerical simulation system for aerocraft aerodynamic characteristics oriented to the intelligent Internet of things is proposed. In the design, the traditional numerical simulation system is used in the hardware design, and only the software part is designed. Firstly, the input interface of calculation parameters of the system is designed, and the hierarchical structure of the software is given. The transport equation of the motion coordinate system is set up to calculate the relevant motion parameters of the input value After correcting the deviation, the calculation method of aerodynamic wind energy is designed, and the final simulation data are obtained. In order to verify whether the designed numerical simulation system can effectively control the numerical deviation, the design system is compared with reference [3], reference [5] and document [7], and connected with the intelligent Internet of things, and the relevant deviation values are extracted and controlled. The experimental results show that the designed numerical simulation system can achieve deviation control quickly, and The feasibility of the design is proved for overshoot.
One-Class Novelty Detection via Sparse Representation with Contrastive Deep Features

We propose a novel one-class novelty detection method by using sparse representation with contrastive deep features. To learn representative deep features from target one- class training data, we generate in-class and pseudo out-of-class training samples at first. Then, shared-weight networks with the contrastive loss are used to learn the contrastive deep features based on the generated training samples. We model the distribution of the learned features of the target class by using sparse dictionary learning. Testing samples are identified based on sparse reconstruction results by using the learned dictionary. The experimental results show the performance of the proposed method is comparable with state-of-the-art methods in the MNIST dataset and outperforms those methods in the CIFAR- 10 dataset.
Street View Image Retrieval with Average Pooling Features

Street view image retrieval is a challenging subject due to the complexity of the street view. Such images contain a large number of similarly structured buildings and are obscured by other objects such as pedestrians and vehicles. In this paper, we present a street view image retrieval system based on average pooling features and SIFT (Scale-invariant feature transform), which extract location information of query images from retrieval results. The average pooling features extracted from the middle layer of the convolutional neural networks (CNNs) are used to retrieve on a large-scale street view dataset firstly, then the SIFT Re-ranking is performing on the results of former retrieval to improve precision. Besides, before performing retrieval panorama street view images are projected into the perspective view images that are similar to the query image captured by mobile. Finally, 82.14% of images are correctly retrieved through our system and the Re-ranking method increases retrieval performance by about 12.5%. The results show that the method can effectively locate the mobile phone image captured in the city by retrieving the large-scale street view image dataset.
Pancreatic Cancer Classification Using the Kernel-based Support Vector Machine (KSVM)

Abstract Pancreatic cancer is a disease in which malignant (cancerous) tumor cells develop in pancreatic tissue; organ behind the lower abdomen and in front of the spine, which helps the body use and store energy from food by producing hormones to control blood sugar levels and digestive enzymes to break down food. Usually, pancreatic cancer is rarely detected at an early stage. One sign of a person with pancreatic cancer is diabetes, especially if it coincides with rapid weight loss, jaundice, or pain in the upper abdomen that spreads to the back. Among various types of cancer, pancreatic cancer has the lowest survival rate of only about 3%-6% of those diagnosed who can survive for five years. If patients are diagnosed on time for treatment, their chances of survival will increase. There is a tumor marker commonly used to follow the course of pancreatic cancer, namely CA 19-9 which can be measured in the blood. Healthy people can have small amounts of CA 19-9 in their blood. High levels of CA 19-9 are often a sign of pancreatic cancer. But sometimes, high levels can indicate other types of cancer or certain noncancerous disorders, including cirrhosis and gallstones. Because a high level of CA 19-9 is not specific for pancreatic cancer, CA 19-9 cannot be used by itself for screening or diagnosis. It can help monitor the progress of your cancer and the effectiveness of cancer treatment. In this study, the Kernel-based Support Vector Machine method is used to classify CA19-9 blood test results into two sections including data on patients diagnosed with pancreatic cancer or normal patients. This method will get an accuracy of around 95%.
Exploiting Adversarial Examples to Drain Computational Resources on Mobile Deep Learning Systems

In order to perform deep learning tasks everywhere, many optimizations have been proposed to address the resource limitations on mobile systems like IoTs. A key approach among others is to dynamically adjust computational resources of the deep learning inference according to the characteristics of incoming inputs. For example, one of popular optimizations is to pick for each input a suitable combination of computations with respect to its inference difficulty. However, we find out that such “dynamic routing” of computations could be exploited to drain/waste precious resources on mobile deep learning systems. In this work, we introduce a new deep learning attack dimension, the computational resources draining, and demonstrate its feasibility in one of possible attack manners, the adversarial examples of input data. We describe how to construct our special adversarial examples aiming to the resource draining, and show that these poisoned inputs are able to increase the computation loads on purpose with two experiment datasets. We hope that our findings can shed light on the path of improving the robustness of mobile deep learning optimizations.
Efficient Trajectory Library Filtering for Quadrotor Flight in Unknown Environments

Quadrotor flight in cluttered, unknown environments is challenging due to the limited range of perception sensors, challenging obstacles, and limited onboard computation. In this work, we directly address these challenges by proposing an efficient, reactive planning approach. We introduce the Bitwise Trajectory Elimination (BiTE) algorithm for efficiently filtering out in-collision trajectories from a trajectory library by using bitwise operations. Then, we outline a full receding-horizon planning approach for quadrotor flight in unknown environments demonstrated at up to 50 Hz on an onboard computer. This approach is evaluated extensively in simulation and shown to collision check up to 4896 trajectories in under 20μs, which is the fastest collision checking time for a MAV planner, to the best of the authors' knowledge. Finally, we validate our planner in over 120 minutes of flights in forest-like and urban subterranean environments.
Vector ordering and regression learning‐based ranking for dynamic summarisation of user videos

Dynamic video summarisation (video skimming) is a process of generating a shorter video (video skim) as a summary of a given video, which helps in its easier and quicker comprehension. In this study, an efficient dynamic summarisation approach for user videos is proposed using vector ordering for ranking video units (frames/shots). User videos are casually shot unscripted videos, where skimming involves the selection of its interesting part(s) ignoring many uninteresting ones. The concept of R-ordering of vectors is employed to find a representative frame, which is used to perform relative ranking of the video frames. It is theoretically shown that significance is given to each element of a frame's feature vector while computing the importance scores that lead to the frame ranks used for skimming. Furthermore, the allocation of different weights to the features involved is also achieved using linear and Gaussian process regressions. Through extensive experiments considering several standard datasets with human-labelled ground truth, the proposed approach is demonstrated to be efficient and to perform better than the relevant state-of-the-art.
Short Duration Aggregate Statistical Model Checking for Multi-Agent Systems

For analysing large and complex systems, Statistical Model Checking has proved to be an attractive alternative to the more expensive numerical model checking approaches. Statistical Model Checking involves Monte Carlo sampling of execution traces of the system. Stochastic multi-agent systems with very large agent populations add significant simulation overheads that dominate the model checking complexity. This offsets some of the advantage in terms of the speed that statistical model checking offers. To mitigate these simulation overheads, we explore an approach based on sampling agent populations in addition to the Monte Carlo sampling of execution traces. We argue that this approach is particularly useful for aggregate queries on Multi-Agent systems that are also restricted in the time horizon–for example, bounded until operators in probabilistic temporal languages. We show that this can result in significant improvement in running times at the expense of only a marginal loss in accuracy and provide empirical evidence for this.
Connecting and linking neurocognitive, digital phenotyping, physiologic, psychophysical, neuroimaging, genomic, &amp; sensor data with survey data

Abstract Combining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse “big data” measures using integrated data-driven cross-discipline system architecture.
Enhancing ontology-based home Care Services platform using Bayesian networks

During the first generation of home intervention and support, complaints were usually triggered by telephone call. In most situations needs where described orally; there were no sufficient information neither on the patient's condition nor on his medical history. Thanks to the advance of technology, especially systems based on data mining, artificial intelligence time is saved, and immediate service is offered. Although home intervention and care have been the subject of numerous studies, the resolution of the overall decision-making problem is not sufficiently developed. Due to the lack of information such as the state of health of a patient, the set of parameters characterizing the daily life habits of the person analyzed in parallel with the evolution of physiological and environmental parameters. In addition to that, it is necessary to take into consideration the Medical Core, his location, his profile not only his professional status, but also his capacities and skills, which are not explicitly described in his curriculum. Different studies and systems exist in literature. Each them takes into account only part of the parameters. Indeed, these studies face either the monitoring of daily activities, the monitoring of physiological data or other environmental part. Either they take into account the specific features of the medical core profile. Either these systems use probabilistic data mining, which involves many interactions with experts to interpret the data, or an expert system based on the rules of inference defined by medical experts. In addition, most do not use controlled vocabulary, which provides semantics to the system. This complicates information sharing and collaborative work. We proposed in a previous work an ontology-based solution [3], the goal is to help the collaborator to make a decision and to draw new information. as the complexity of the data increases, so does the need to deal with uncertainty. Several approaches to the representation and reasoning of uncertainty in the semantic web have emerged. This article is a study for the integration of uncertainty in e-SAAD ontologies expressed in Web ontology language.
Differential Privacy Preserving Naive Bayes Classification via Wavelet Transform

While data mining brings convenience to people, it also leads to the leakage of users' privacy. It is very meaningful to mine data while protecting user data privacy. The existing Naive Bayes classification algorithms based on differential privacy have low utility in classifying high-dimensional datasets. To solve this problem, we propose a differential privacy preserving Naive Bayes classification algorithm via wavelet transform. We perform wavelet transform on the original dataset. By retaining the approximate coefficients after wavelet transform, the purpose of data dimensionality reduction is achieved. Laplace noise is added to the dimensionality-reduced dataset, and then perform Naive Bayes classification on the noisy dataset. Experimental results show that the classification accuracy of our proposed algorithm for high-dimensional datasets is significantly better than the existing differential private Naive Bayes classification algorithm.