Reviewerly/exHarmony huggingface dataset:
Predicting the existence of exploitation concepts linked to software vulnerabilities using text mining

Software vulnerabilities are weaknesses of specific products and versions that may lead in benefiting attackers to exploit such malfunctions, having a further goal to gain access to operating systems, devices and users’ data. As not all vulnerabilities constitute potential threats to such information, this research attempts to explore ways to the identify the ones that are possible to be exploited using only textual descriptions. The practical goal of the experiments is to examine future raw descriptions in order to predict whether the linked product is likely to be exploited or not. The ground truth examined is the existence or absence of references that report exploitation concepts of the related weaknesses. To meet our objectives, in this study, we make use of Natural Language Processing (NLP) techniques, feature evaluation filtering mechanisms and an oversampling method in order to adapt the raw texts into inputs to classification models and detect the most important terms. The results are promising as many constructed models provided an overall accepted accuracy based on the information of the collected dataset.
Garbage Classification and Recognition System Based on Neural Network

Traditional garbage classification has many errors and consumes resources seriously, which no longer meets the current needs of garbage classification. In response to these problems, this research proposes a garbage classification model based on migration learning and the application of this model to the actually created garbage separation device. First, pre-train the ResNet50 deep network learning model on the ImageNet dataset; Secondly, transfer the underlying features shared by the edges, colors, and textures learned by the convolution module of the ResNet50 deep network model to the residual network layer of the network model for garbage classification as the initialization parameters; Then use the extracted feature map as input to train the garbage classification model; Finally, modify the fully connected layer to a four-classification problem to accurately classify the garbage. By using the improved Trashnet training set to compare the three pre-training networks, namely alexnet, googlenet and resnet50, the results show that resnet50 has a relatively good recognition accuracy. After fine-tuning the training parameters and the training set, the final verification rate is 91.42%. which basically meet the accuracy requirements of garbage classification. At the same time, this garbage sorting network is implanted into the STM32F4 single-chip microcomputer through the Raspberry Pi to obtain a rubbish sorting device that can identify garbage. The photos recorded by the camera in real time are transmitted to the single-chip microcomputer for processing to obtain the classification result. At this time, the motor starts to work so that the garbage can fall into the corresponding bucket. While reducing the consumption of human and material resources, it greatly improves the accuracy of garbage classification and provides a new method for garbage classification.
A Markov Model for Improving the Performance of COVID-19 Contact Tracing App

Nowadays, the world is betting on mobile phone applications to stop the spread of deadly infectious diseases, including the COVID-19 pandemic and its new variants. Since the beginning of the COVID-19 outbreak, a group of countries has launched contact-tracing apps to stem the spread of COVID-19. The app helps health authorities to track the movements of people diagnosed with the virus, which gives a chance to isolate them rather than isolate the whole population. When two users are near each other, their phones exchange tokens via a Bluetooth connection, recording that they’ve had a close contact. However, when two or more phones send their tokens simultaneously to look for another phone, collisions may occur. Therefore, the user may not get the warning notifications even if he was near someone diagnosed with COVID-19. To overcome this problem, we propose a mechanism to improve the Bluetooth network performance. The goal of this improvement is to make the contact tracing apps more efficient. A Markov chain model is then constructed to evaluate the system performance. Numerical results demonstrate that our mechanism can significantly improve Bluetooth performance and improve the contact tracing app’s performance.
The rise and fall of Electra: emergence and transformation of a global cryptocurrency community

Electra was developed by an unknown man, Electra01. Following its emergence in cryptocurrency markets in 2018, its market capitalization briefly reached 136 billion USD, exceeding Bitcoin in value. The project’s community of 20,000 individuals, wrote its white papers, updated its blockchain, instituted a foundation, introduced a payment system, and voted to be the best crypto project in 2020 in the world by a global vote. Following a fundamental controversy in its community, Electra collapsed as the founder sold his hundreds of millions of Electras in November 2020, effectively killing the project. Within a short period, the community left the founder behind, and moved on to a new project, giving (re)birth to their community money, this time called Electra Protocol. Drawing on an empirical case study, this paper presents an analysis of how cryptocurrency communities emerge, mature and migrate as they make data monies.
Distributed Cost-Aware Fault-Tolerant Load Balancing in Geo-Distributed Data Centers

Many critical e-commerce and financial services running on distributed data centers require high availability. Recent surveys show that the frequency and duration of failures (either partial or complete) at a data center are increasing over the years. However, most of the literature does not address fault-tolerance in data centers. In this paper, we address the problem of load balancing in fault-tolerant data centers while minimizing the operating cost (due to energy consumption). We formally model the problem using linear optimization and propose a distributed two-stage algorithm to solve it. In the first stage, the proposed workload shifting algorithm distributes the load on a failed data center across the remaining ones with a marginal increase in the operating cost. Subsequently, the min-cost network flow model is used to derive a request mapping policy, where the quality of service (QoS) requirements are considered using a set of constraints on the delay. We evaluate the proposed algorithms using real-world data for the demand and the energy consumption cost (both brown and green energy). Results show that the proposed algorithm has low computational complexity, yet exactly gives the cost obtained by the global optimal solution.
Research on segmentation of MRI brain tumor image based on improved UNet3+

According to the characteristics of multimodality, complexity and computer-aided segmentation of MRI brain tumor images, an improved UNet3+ brain tumor segmentation network based on full-scale jump connection is proposed. The main work is as follows: the residual structure is integrated into the feature extraction part of the network, and then the data correlation upsampling structure called Dupsampling is integrated into the upsampling part of the network to improve the quality of the feature map and prevent the gradient from disappearing. Finally, the hybrid loss function combined with cross entropy and dice is used to further improve the segmentation accuracy. The method was validated on BraTs2019 partial data, and the dice coefficients of the whole tumor, core tumor and enhanced tumor are 0.9186, 0.8419 and 0.8358 respectively, and the Hausdorff distance is 2.097, 3.672 and 2.008, which are better than most brain tumor segmentation models.
AI-human interactive pipeline with feedback to accelerate medical image annotation

We propose an AI-human interactive pipeline to accelerate medical image annotation of large data sets. This pipeline continuously iterates on three steps. First, an AI system provides initial automated annotations to image analysts. Second, the analysts edit the annotations. Third, the AI system is upgraded with analysts’ feedback, thus enabling more efficient annotation. To develop this pipeline, we propose an AI system and upgraded workflow that is focused on reducing the annotation time while maintaining accuracy. We demonstrated the ability of the feedback loop to accelerate the task of prostate MRI segmentation. With the initial iterations on small batch sizes, the annotation time was reduced substantially.
Investigating a potential format effect with two-column proofs

Abstract The current study aims to explore the impact of the two-column format in writing simple mathematical arguments. That is to say, a structured method of presenting a mathematical proof or argument by using a tabular layout with two columns. The underlying goal of the research reported in this paper is to inform understanding of how to effectively assess students’ proof construction, particularly online. In this paper, we report the results of an experiment designed to test the hypothesis that students provide more justifications when using the two-column format in writing mathematical proofs. The participants were 80 first year undergraduate mathematics students at the University of Edinburgh who were enrolled in a calculus course. The findings supported this hypothesis: overall frequency of justification were higher in the two-column condition compared with the traditional condition. This is due to the format structure where students are expected to justify each line in the statements’ column with a corresponding explanation in the justification column. However, as a side effect of increasing the amount of justifications, students in the two-column group generated more non-explanation statements which we classed as paraphrasing. A secondary outcome was our use and further development of the coding scheme.
A New Family of Exceptional Rational Functions

For each odd prime power q, we construct an infinite sequence of rational functions f(X) in F_q(X), each of which is exceptional, which means that for infinitely many n the map c-->f(c) induces a bijection of P^1(F_{q^n}). Moreover, each of our functions f(X) is indecomposable, which means that it cannot be written as the composition of lower-degree rational functions in F_q(X). In case q is not a power of 3, these are the first known examples of indecomposable exceptional rational functions f(X) over F_q which have non-solvable monodromy groups and have arbitrarily large degree. These are also the first known examples of wildly ramified indecomposable exceptional rational functions f(X), other than linear changes of polynomials.
Streaming big time series forecasting based on nearest similar patterns with application to energy consumption

Abstract This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbours algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbours algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and a forecasting model based on the nearest neighbours is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbours model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of $10$ minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy.
A Survey on Digital Forensics in Cloud Environment Using Blockchain Technology

Today, the best progress in data innovation is likely to be distributed computing. It presents many pleasant mechanical and financial advantages in the new world. There is no uncertainty that the new distributed computing ideas to various clients. Many cloud clients stay reluctant to move their own business to the cloud and the primary consideration of a cloud client is cloud security and the danger of the obscure. Cloud stage adds to the difficulties of actual area, investigation, safeguarding of computerized proof. An advanced legal issue in distributed computing brings new specialized and lawful difficulties to give computerized figuring and arrangements. There are tons of difficulties in the field of criminology examination that is looked by the specialist which may muddle in the extraction of confirmations. In distributed computing the issues are clarified utilizing the periods of advanced criminology as the root. In this paper we systematically measure the scientific difficulties in cloud digital forensics and examine new techniques used by block chain arrangements and advancements to perform good examinations. A few new examination challenges tending to this changing setting are additionally distinguished and talked about.
Coupled Training for Multi-Source Domain Adaptation

Unsupervised domain adaptation is often addressed by learning a joint representation of labeled samples from a source domain and unlabeled samples from a target domain. Unfortunately, hard sharing of representation may hurt adaptation because of negative transfer, where features that are useful for source domains are learned even if they hurt inference on the target domain. Here, we propose an alternative, soft sharing scheme. We train separate but weakly-coupled models for the source and the target data, while encouraging their predictions to agree. Training the two coupled models jointly effectively exploits the distribution over unlabeled target data and achieves high accuracy on the target. Specifically, we show analytically and empirically that the decision boundaries of the target model converge to low-density "valleys" of the target distribution. We evaluate our approach on four multi-source domain adaptation (MSDA) benchmarks, digits, amazon text reviews, Office-Caltech and images (DomainNet). We find that it consistently outperforms current MSDA SoTA, sometimes by a very large margin.
Product Requirement Information Using TF-IDF and Naive Bayes Techniques as a Remedy for Stakeholders in IT-Enterprises

Writing adequate requirements necessitates a delicate sensibility on the researcher's part. It is not uncommon for requirements to go through numerous iterations before all stakeholders understand them. In this research study, unspecified requirements are detected as incomplete written functional and non-functional requirements and analyzed using a specification matrix. The matrix presents a new metric for assessing whether requirements are fully specified, moderately specified, or unspecified. The specification matrix is put through a UseCase within Deutsche Telekom (DT) AG. The unspecified requirements are subjected to a semantic review using a requirements stencil, referred to as adequate product requirement information (PRI). The adequate PRI are automatically classified by categories, including chapters, using statistical approaches (TF-IDF and Naive Bayes probability calculation). To avoid the propagation of unspecified requirements, this work seeks to evaluate stakeholder-oriented novel solutions utilizing the specification matrix and the TF-IDF with the Naive Bayes probability computation.
Design of the operation of a rotating machine for the acquisition of multi-view stereoscopic images for the 3D reconstruction of objects

This work presents a machine to locate a stereoscopic camera in different positions around an object to acquire a sequence of images that allows the reconstruction of such object through artificial vision algorithms. The GEMMA guide was used to define the modes of operation of the proposed machine. In addition, the mechanical and electronic elements that make up the machine and the programming logic for its control with PLC were also defined. It was demonstrated using a graphical interface that the mode of operation of the machine is carried out satisfactorily. Additionally, this work presents synthetic image results to represent a sequence of images acquired from different points of view considering different levels of elevation of the camera, showing the type of results obtained with the proposed machine.
Classification of Breast Cancer using User-Defined Weighted Ensemble Voting Scheme

When weak classifiers, i.e., estimators, are not justifying the classification of breast cancer then ensemble learning is a way to improve the classification of cancer. The ensemble is basically an aggregator where all weak classifiers are merged to get a strong classifier. The ensemble is based on a majority voting scheme. A hard voting scheme is used to take a major vote of each classifier whereas a soft voting scheme takes the weights of the probability of each classifier. A custom based weights are assigned in this paper and the final classification of cancer using ensemble classifier is outperformed than each estimator. The highest accuracy from the proposed ensemble classifier is achieved up to 96.47% where the lowest estimator got 93.18 %. The AUC score of ensemble classifier achieved is 0.9948 which is one of the highest among all other estimators.
A polar-edge context-aware (PECA) network for mirror segmentation

This paper presents Polar-Edge Contrast-Aware Network (PECA) for mirror instance segmentation in images of indoor scenes. General instance segmentation methods typically rely on the surface appearance of the object to identify the foreground from background. However, these approaches do not directly apply to mirrors as the mirror surfaces are less reliable due to reflections of the surroundings. On the other hand, the existing saliency-based mirror segmentation methods are prone to predict false positives in images with no mirrors, especially for the indoor scenes that have mirror-shape objects, such as doors and windows. In this work, we propose a novel boundary-based mirror localization method PECA that achieves both high segmentation accuracy on mirrors and low false positive rate on negative samples. PECA uses a context-aware module to extract features along the instance contour and in this way incorporates boundary information for improving mirror detection. The predicted mirror candidates are further refined with a local contrast module for the final mirror instance segmentation. PECA achieves IoU 80.29% on the benchmark Mirror Segmentation dataset (MSD), outperforming the state-of-the-art method MirrorNet (IoU = 78.95%) by 1.34%. It also produces a significantly smaller false positive rate (43.37%) than existing methods (91.39%) on our challenging Negative Mirror Dataset (NMD) without retraining. After training on both MSD and NMD training sets, our model further reduces the false positive rate to 0.08% on NMD testing set, while keeping IoU of 73.07% on MSD, enabling realistic real-world applications.
Policy-driven Data Sharing over Attribute-Based Encryption supporting Dual Membership

Attribute-Based Encryption (ABE) plays an important role in current secure data sharing through fine-grained customizable policies. However, the existing ABE schemes only support simple predicates, = and ≠, but cannot express a more general membership predicates, ∈ and ∉, in policies. The low expressivity of ABE will enlarge the ciphertext storage and reduce the communication efficiency. To overcome this problem, we propose an ABE supporting Dual Membership (DM-ABE). The core problem for implementing this scheme is how to use cryptographic methods to decide the membership between the verified element and the given set. In order to solve this problem, we design a cryptographic algorithm, called Secure Decision of Membership (SDM), based on aggregation functions. In this algorithm, any set can be aggregated into one cryptographic element, and the verified element and the given set can be converted into another cryptographic element in decision process. The membership between them can be decided by the above two cryptographic elements. Furthermore, we construct the DM-ABE by using SDM. Because of the good expressivity of our DM-ABE, we further propose a novel cryptographic data sharing framework by integrating DM-ABE and attribute-based access control to provide fine-grained access control and security protection for private data. In the security proof of DM-ABE, we prove that the DM-ABE satisfies the semantic security against chosen-plaintext attacks under the DBDHE assumption in the standard model through a unified way, considering both two encryption methods for ∈ and ∉ at the same time. Finally, we analyze our scheme in terms of time and space complexity, and compare it with some existing schemes. The results show that our DM-ABE has a better expressive ability on the boolean logic of general membership predicates, ∈ and ∉.
Study on Efficient Service Broker Policy for Data Center Selection Analyst for Education System Using Cloud Computing

Cloud computing is an area that is quickly getting differentiation in both shrewd world and industry. Cloud Analyst is huge mechanical gathering to show and separate cloud computing condition and applications before certified relationship of cloud things. Capriciously picked server farm is inclined to give prohibited outcomes in term of reaction time, asset use, cost or different cutoff points. In this paper we propose a need-based Round-Robin administration merchant figuring which passes on the mentioning dependent on the need of server farms and gives ideal execution over the standard Discretionary choice calculation. Education is a need of everybody these days for moderate new development. It anticipates that for the future, an enormous portion could establish a healthy society. Advancement for high-quality education anticipates a main trend. Starting now, cloud computing is the most unexpected and solid asset for exchanging knowledge, as it works in various areas such as clinical management, business, correspondence and more. It also occurs in the scholarly organization, as it offers better benefits on request and numerous other monumental and strong inclinations. Many expert co-ops offer specialized cloud-based software so consumers can work on such apps according to their needs without a completely shocking stretch. In this review article, we will propose a model to educators and understudies with the aid of cloud computing so that teachers can share the course materials over the cloud and understudies can get the information with the help of this model about their assessments, activities and various items. With the aid of this model, guard/gatekeepers may also review the evaluation content, participation records, and so on of their young people. This review paper therefore focuses on how cloud computing can be used in the context of the arrangement to boost the difficulties in the preparation of foundations.