Reviewerly/exHarmony huggingface dataset:
A Generalized Deep Learning Algorithm Based on NMF for Multi-View Clustering

Multi-view clustering research is a hot topic in the field of data mining, where complementary information between views can better describe data objects and improve the clustering performance. Non-negative matrix factorization (NMF) based multi-view clustering algorithm suffers from weak feature extraction, slow convergence speed and low accuracy. To solve these problems, this paper proposes a generalized deep learning multi-view clustering (GDLMC) algorithm based on NMF. Firstly, via decoupling the elements in the matrix, the matrix elements are non-negatively restricted using an activation function with a non-negative value domain, and the elements are updated employing stochastic gradient descent with learning rate guidance. Then, the corresponding gradients when the elements update are transformed into generalized weights and generalized biases, followed by combining the generalized weights and generalized biases with activation functions to construct generalized deep learning (GDL). Further, GDL is adopted to learn the corresponding low-dimensional matrix of each view and consensus matrix for obtaining the GDLMC algorithm. In addition, the detailed reasoning of the GDLMC algorithm are given. Finally, extensive experiments are conducted on four public datasets including regular and large-scale datasets, and the experimental results show that GDLMC has significant advantages.
An Efficient Identity-Based Provable Data Possession Protocol With Compressed Cloud Storage

Cloud storage is more and more prevalent in practice, and thus how to check its integrity becomes increasingly essential. A classical solution is identity-based (ID-based) provable data possession (PDP), which supports certificateless cloud storage auditing without entire user data. However, existing ID-PDP protocols always require that cloud users outsource data blocks, authenticators and a small-sized file tag to the cloud, and make use of the heavy elliptic curve cryptography over bilinear pairing. These disadvantages would result in vast storage, communication, and computation costs, which is unexpected, especially for resource-limited cloud users. To improve the performance, this paper proposes a novel cryptographic primitive: ID-based PDP with compressed cloud storage. In this model, cloud storage auditing can be achieved by using only encrypted data blocks in a self-verified way, and original data blocks can be reconstructed from the outsourced data. Thus, data owners no longer need to store original data blocks on the cloud. We also use some basic algebraic operations to realize a concrete ID-based PDP protocol with compressed cloud storage, which is quite efficient due to no heavy cryptographic operations involved. The proposed protocol can easily be extended to support the other practical functions by using the primitive replacement technique. The proposed protocol is strictly proven to have the properties of correctness, privacy, unforgeability and detectability. Finally, we give plenty of theoretical analysis and experimental results to validate the efficiency of the proposed protocol.
Data Trusts as a Service: Providing a platform for multi‐party data sharing

The major setback for data sharing is that data producers (i.e., subjects) are not able to determine how their data is being used/misused by data collectors (custodians). In view of this challenge, data trusts have been suggested to solve the problem of mistrust in multi-partner data sharing scenarios. In this research, we proposed Data Trusts as a Service (DTaaS), a cloud-based platform designed to support multi-partner data sharing that allows data subjects to see where their data are. They can also use the designed DTaaS to control who can access their information and what it can be used for based on policies/contracts. The DTaaS uses blockchain to enforce provenance and auditability, events to monitor data streams, and visualization to analyze workflows. Evaluations of the implementation of DTaaS show that the system is transparent, enables data subjects to track their data in a multi-data sharing environment, and promotes trustworthiness between stakeholders.
Analysis and Improvement of Differential Computation Attacks against Internally-Encoded White-Box Implementations

White-box cryptography is the last security barrier for a cryptographic software implementation deployed in an untrusted environment. The principle of internal encodings is a commonly used white-box technique to protect block cipher implementations. It consists in representing an implementation as a network of look-up tables which are then encoded using randomly generated bijections (the internal encodings). When this approach is implemented based on nibble (i.e. 4-bit wide) encodings, the protected implementation has been shown to be vulnerable to differential computation analysis (DCA). The latter is essentially an adaptation of differential power analysis techniques to computation traces consisting of runtime information, e.g., memory accesses, of the target software. In order to thwart DCA, it has then been suggested to use wider encodings, and in particular byte encodings, at least to protect the outer rounds of the block cipher which are the prime targets of DCA.In this work, we provide an in-depth analysis of when and why DCA works. We pinpoint the properties of the target variables and the encodings that make the attack (in)feasible. In particular, we show that DCA can break encodings wider than 4-bit, such as byte encodings. Additionally, we propose new DCA-like attacks inspired from side-channel analysis techniques. Specifically, we describe a collision attack particularly effective against the internal encoding countermeasure. We also investigate mutual information analysis (MIA) which naturally applies in this context. Compared to the original DCA, these attacks are also passive and they require very limited knowledge of the attacked implementation, but they achieve significant improvements in terms of trace complexity. All the analyses of our work are experimentally backed up with various attack simulation results. We also verified the practicability of our analyses and attack techniques against a publicly available white-box AES implementation protected with byte encodings –which DCA has failed to break before– and against a “masked” white-box AES implementation –which intends to resist DCA.
Human Activity Recognition by Sequences of Skeleton Features

In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person's body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people's activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.
Automatic detection of the parasite <i>Trypanosoma cruzi</i> in blood smears using a machine learning approach applied to mobile phone images

Chagas disease is a life-threatening illness caused by the parasite Trypanosoma cruzi. The diagnosis of the acute form of the disease is performed by trained microscopists who detect parasites in blood smear samples. Since this method requires a dedicated high-resolution camera system attached to the microscope, the diagnostic method is more expensive and often prohibitive for low-income settings. Here, we present a machine learning approach based on a random forest (RF) algorithm for the detection and counting of T. cruzi trypomastigotes in mobile phone images. We analyzed micrographs of blood smear samples that were acquired using a mobile device camera capable of capturing images in a resolution of 12 megapixels. We extracted a set of features that describe morphometric parameters (geometry and curvature), as well as color, and texture measurements of 1,314 parasites. The features were divided into train and test sets (4:1) and classified using the RF algorithm. The values of precision, sensitivity, and area under the receiver operating characteristic (ROC) curve of the proposed method were 87.6%, 90.5%, and 0.942, respectively. Automating image analysis acquired with a mobile device is a viable alternative for reducing costs and gaining efficiency in the use of the optical microscope.
Many researchers say they’ll share data — but don’t

Reasons included a lack of informed consent or ethics approval to share; misplaced data; and that others had moved on from the project. Reasons included a lack of informed consent or ethics approval to share; misplaced data; and that others had moved on from the project.
HiTKG: Towards Goal-Oriented Conversations via Multi-Hierarchy Learning

Human conversations are guided by short-term and long-term goals. We study how to plan short-term goal sequences as coherently as humans do and naturally direct them to an assigned long-term goal in open-domain conversations. Goal sequences are a series of knowledge graph (KG) entity-relation connections generated by KG walkers that traverse through the KG. The existing recurrent and graph attention based KG walkers either insufficiently utilize the conversation states or lack global guidance. In our work, a hierarchical model learns goal planning in a hierarchical learning framework. We present HiTKG, a hierarchical transformer-based graph walker that leverages multiscale inputs to make precise and flexible predictions on KG paths. Furthermore, we propose a two-hierarchy learning framework that employs two stages to learn both turn-level (short-term) and global-level (long-term) conversation goals. Specifically, at the first stage, HiTKG is trained in a supervised fashion to learn how to plan turn-level goal sequences; at the second stage, HiTKG tries to naturally approach the assigned global goal via reinforcement learning. In addition, we propose MetaPath as the backbone method for KG path representation to exploit the entity and relation information concurrently. We further propose Multi-source Decoding Inputs and Output-level Length Head to improve the decoding controllability. Our experiments show that HiTKG achieves a significant improvement in the performance of turn-level goal learning compared with state-of-the-art baselines. Additionally, both automatic and human evaluation prove the effectiveness of the two-hierarchy learning framework for both short-term and long-term goal planning.
Detecting Latent Topics and Trends in Software Engineering Research Since 1980 Using Probabilistic Topic Modeling

The landscape of software engineering research has changed significantly from one year to the next in line with industrial needs and trends.Therefore, today's research literature on software engineering has a rich and multidisciplinary content that includes a large number of studies; however, not many of them demonstrate a holistic view of the field.From this perspective, this study aimed to reveal a holistic view that reflects topics, trends, and trajectories in software engineering research by analyzing the majority of domain-specific articles published over the last 40 years.This study first presents an objective and systematic method for corpus creation through major publication sources in the field.A corpus was then created using this method, which includes 44 domain-specific conferences and journals and 57,174 articles published between 1980 and 2019.Next, this corpus was analyzed using an automated text-mining methodology based on a probabilistic topic-modeling approach.As a result of this analysis, 24 main topics were found.In addition, topical trends in the field were revealed.Finally, three main developmental stages of the field were identified as: the programming age, the software development age, and the software optimization age.
Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
Digital pathology – Rising to the challenge

Digital pathology has gone through considerable technical advances during the past few years and certain aspects of digital diagnostics have been widely and swiftly adopted in many centers, catalyzed by the COVID-19 pandemic. However, analysis of requirements, careful planning, and structured implementation should to be considered in order to reap the full benefits of a digital workflow. The aim of this review is to provide a practical, concise and hands-on summary of issues relevant to implementing and developing digital diagnostics in the pathology laboratory. These include important initial considerations, possible approaches to overcome common challenges, potential diagnostic pitfalls, validation and regulatory issues and an introduction to the emerging field of image analysis in routine.
Deep learning based single sample face recognition: a survey

Face recognition has long been an active research area in the field of artificial intelligence, particularly since the rise of deep learning in recent years. In some practical situations, each identity has only a single sample available for training. Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models. Therefore, in recent years, researchers have attempted to unleash more potential of deep learning and improve the model recognition performance in the single sample situation. While several comprehensive surveys have been conducted on traditional single sample face recognition approaches, emerging deep learning based methods are rarely involved in these reviews. Accordingly, we focus on the deep learning-based methods in this paper, classifying them into virtual sample methods and generic learning methods. In the former category, virtual images or virtual features are generated to benefit the training of the deep model. In the latter one, additional multi-sample generic sets are used. There are three types of generic learning methods: combining traditional methods and deep features, improving the loss function, and improving network structure, all of which are covered in our analysis. Moreover, we review face datasets that have been commonly used for evaluating single sample face recognition models and go on to compare the results of different types of models. Additionally, we discuss problems with existing single sample face recognition methods, including identity information preservation in virtual sample methods, domain adaption in generic learning methods. Furthermore, we regard developing unsupervised methods is a promising future direction, and point out that the semantic gap as an important issue that needs to be further considered.
Deploying decentralized, privacy-preserving proximity tracing

Lessons from a pandemic.
Real time social distance detection using Deep Learning

In the Covid-19 pandemic, if residents do not take action to prevent the virus from spreading, the process of softening the curve of the coronavirus will be complicated in the face of the worldwide Covid-19 scenario. Without vaccination, the only method to combat the disease is social isolation. The proposed system employs the You Only Look Once, Version 3 (YOLOv3) object detection model to identify persons in the background and bind boxes around them, and assign IDs for in-depth tracking of recognized people. This study focuses on public space surveillance and determining whether or not people maintain social distance as per Covid-19 guidelines. YOLOv3 is an efficient tracking method that produces positive results with a moderate mean Average Precision(MAP) and Frame Per Second (FPS) score for monitoring community deviations in real-time. In this study, YOLOv3 is used for object capture, and the OpenCV library is used for image processing. Proposed work is helpful in areas where big crowds are expected, such as retail malls, movie theatres, railway stations, airports, and public places.
Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning

Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.
Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios

Multi-view learning makes use of diverse models arising from multiple sources of input or different feature subsets for the same task. For example, a given natural language processing task can combine evidence from models arising from character, morpheme, lexical, or phrasal views. The most common strategy with multi-view learning, especially popular in the neural network community, is to unify multiple representations into one unified vector through concatenation, averaging, or pooling, and then build a single-view model on top of the unified representation. As an alternative, we examine whether building one model per view and then unifying the different models can lead to improvements, especially in low-resource scenarios. More specifically, taking inspiration from co-training methods, we propose a semi-supervised learning approach based on multi-view models through consensus promotion, and investigate whether this improves overall performance. To test the multi-view hypothesis, we use moderately low-resource scenarios for nine languages and test the performance of the joint model for part-of-speech tagging and dependency parsing. The proposed model shows significant improvements across the test cases, with average gains of -0.9 ∼ +9.3 labeled attachment score (LAS) points. We also investigate the effect of unlabeled data on the proposed model by varying the amount of training data and by using different domains of unlabeled data.
Path planning of unmanned surface vessel in an unknown environment based on improved D*Lite algorithm

To solve the problem of path planning for an unmanned surface vessel in an unknown environment, this paper proposes a path planning algorithm based on the improved D*Lite algorithm. First, to improve the computational efficiency of the traditional D*Lite algorithm, the path cost function is improved to reduce the expansion range of nodes, and the node expansion direction is limited to avoid double node computation. Second, the path planned by the traditional D*Lite algorithm on the grid map is suboptimal; so, the inverse distance weighted (IDW) interpolation method is applied to shorten the path length. To improve the smoothness of the planned path by the traditional D*Lite algorithm, the Dubins algorithm is introduced for local path smoothing, and a smooth collision-free path that conforms to the motion dynamics of the unmanned surface vessel is obtained. The proposed algorithm was verified by experiments in known and unknown environments. The performance of the proposed algorithm was compared with the traditional D*Lite algorithm in terms of planning time, path length, and path smoothness. The results revealed that the proposed algorithm has the shortest path length, a shorter planning time, and the best smooth path.
Performance Analysis of Hyperledger Besu in Private Blockchain

In this work, we present a set of comprehensive experimental studies on Hyperledger Besu in private blockchain. We aim to exhibit its performance characteristics in terms of transaction throughput, latency, resource utilization, and scalability, from the application perspective by adding a load balancer middleware. We have carefully designed a set of comparative experiments and judiciously selected typical parameters, including transaction send rate, network size, node flavor, load balancing, consensus, and block time. In particular, three proof of authority consensus algorithms, Clique, IBFT 2.0, and QBFT, are investigated. Through extensive experimental evaluations using the Hyperledger Caliper benchmark tool, we analyze how these parameters impact the performance of a private Besu blockchain. Our studies reveal several interesting findings: 1) Blockchain parameters, e.g., block time and block size, are the most significant factors in determining Besu performance; 2) The performance of Besu is bottlenecked by transaction execution and blockchain state updates, which are determined by parameters such as node computation power, transaction complexity, and load balancing; 3) A Besu network with QBFT consensus can scale up to 14 validators without noticeable performance loss. Our findings shed some light on further performance improvement of Hyperledger Besu. The identified bottlenecks and root cause analysis provide insightful suggestions for blockchain practitioners to build performant enterprise applications.