Reviewerly/exHarmony huggingface dataset:
Context-aware incremental learning-based method for personalized human activity recognition

Abstract This study introduces an ensemble-based personalized human activity recognition method relying on incremental learning, which is a method for continuous learning, that can not only learn from streaming data but also adapt to different contexts and changes in context. This adaptation is based on a novel weighting approach which gives bigger weight to those base models of the ensemble which are the most suitable to the current context. In this article, contexts are different body positions for inertial sensors. The experiments are performed in two scenarios: (S1) adapting model to a known context, and (S2) adapting model to a previously unknown context. In both scenarios, the models had to also adapt to the data of previously unknown person, as the initial user-independent dataset did not include any data from the studied user. In the experiments, the proposed ensemble-based approach is compared to non-weighted personalization method relying on ensemble-based classifier and to static user-independent model. Both ensemble models are experimented using three different base classifiers (linear discriminant analysis, quadratic discriminant analysis, and classification and regression tree). The results show that the proposed ensemble method performs much better than non-weighted ensemble model for personalization in both scenarios no matter which base classifier is used. Moreover, the proposed method outperforms user-independent models. In scenario 1, the error rate of balanced accuracy using user-independent model was 13.3%, using non-weighted personalization method 13.8%, and using the proposed method 6.4%. The difference is even bigger in scenario 2, where the error rate using user-independent model is 36.6%, using non-weighted personalization method 36.9%, and using the proposed method 14.1%. In addition, F1 scores also show that the proposed method performs much better in both scenarios that the rival methods. Moreover, as a side result, it was noted that the presented method can also be used to recognize body position of the sensor.
An Unsupervised Content-Based Article Recommendation System Using Natural Language Processing

Recommendation system is a system that recommends things which are of interest to the user. This paper discusses two approaches based on content-based recommendation systems. The two approaches involve retrieving similar articles by means of cosine similarity and clustering, later the results of the respective approaches are compared. The text data with which this paper deals with comprises of two hundred and thirty Web articles related to various disciplines of machine learning and data science such as natural language processing, reinforcement learning, and deep learning. The data preprocessing steps involved in the methodology are removal of stopwords and word vectorization using TF-IDF. Utilizing cosine similarity, K-means, and agglomerative clustering methods to retrieve articles of user interest and relevance is based on the articles read by the user.
Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising

Online advertising is becoming an important direction in the advertising industry with its strengths in diverse users, strong interactions, real-time feedback, and expandability.Online advertisement (Ads) can show great marketing ability by processing data from multiple channels to convey information, understanding what users want, and approaching them easily.Moreover, predicting the click-through rate (CTR) can increase advertisement revenue and user satisfaction.However, advertising data contains many features, and the amount is growing rapidly.This can be alleviated through the segmentation of users with similar interests.We assumed that the change of interest of a user could be predicted by other users' change of interest.More specifically, similar users will change their interest in a similar direction.On the basis of this idea, we proposed a novel model, the Deep User Segment Interest Network, to improve CTR prediction.We suggested three novel layers for improving performance: i) an individual interest extractor, ii) a segment interest extractor, and iii) a segment interest activation.These layers captures the latent interest of each user and creates the expressive interest representation of the segment by aggregating each user's interest.We conducted experiments using TaoBao data, which are a kind of real commercial data from an advertising platform, to confirm the CTR prediction improvement by reflecting the segment interest.The proposed algorithm obtained an AUC gain of 0.0029 with a behavior sequence length of 100.This performance exhibited the greatest improvement over other baselines, indicating the proposed method's potential contribution to business improvement.
RETRACTED: Searching and learning english translation long text information based on heterogeneous multiprocessors and data mining

Cooperation with the entire language information retrieval aims to develop intelligent models that use non-compliant and English. It has been constructed to construct the translation model extraction of similar Source Language Sentences Extraction (SLSE). The SLSE, by creating a new pipeline of SLSE through the establishment of the network described above, the first bilingual dictionary of the model of training data, including the Deep Neural Network (DNN) that has been proposed, because it is often in such, it is building because it plays a very important role in the model, SLSE is evaluated by people to ensure the richness of the content that has been used as training data. If the English applicable, not built from 60 gigabytes of text. Since the processing time for it when not highly structured data mining is large, it is troublesome to collect data on this scale. To shorten the processing time, data mining is implemented as a whole.
Accessibility of Command Line Interfaces

Command-line interfaces (CLIs) remain a popular tool among developers and system administrators. Since CLIs are text-based interfaces, they are sometimes considered accessible alternatives to predominantly visual developer tools like IDEs. However, there is no systematic evaluation of the accessibility of CLIs in the literature. In this paper, we describe two studies with 12 developers on their experience of using CLIs with screen readers. Our findings show that CLIs have their own set of accessibility issues - the most important being CLIs are unstructured text interfaces. Based on our results, we provide a set of recommendations for improving the accessibility of command-line interfaces.
CPM R-CNN: Calibrating Point-guided Misalignment in Object Detection

In object detection, offset-guided and point-guided regression dominate anchor-based and anchor-free method separately. Recently, point-guided approach is introduced to anchor-based method. However, we observe points predicted by this way are misaligned with matched region of proposals and score of localization, causing a notable gap in performance. In this paper, we propose CPM R-CNN which contains three efficient modules to optimize anchor- based point-guided method. According to sufficient evaluations on the COCO dataset, CPM R-CNN is demonstrated efficient to improve the localization accuracy by calibrating mentioned misalignment. Compared with Faster R-CNN and Grid R-CNN based on ResNet-101 with FPN, our approach can substantially improve detection mAP by 3.3% and 1.5% respectively without whistles and bells. Moreover, our best model achieves improvement by a large margin to 49.9% on COCO test-dev. Code is available at https://github.com/zhubinQAQ/CPM-R-CNN.
The Number of Almost Perfect Nonlinear Functions Grows Exponentially

Almost perfect nonlinear (APN) functions play an important role in the design of block ciphers as they offer the strongest resistance against differential cryptanalysis. Despite more than 25 years of research, only a limited number of APN functions are known. In this paper, we show that a recent construction by Taniguchi provides at least $$\frac{\varphi (m)}{2}\left\lceil \frac{2^m+1}{3m} \right\rceil $$ inequivalent APN functions on the finite field with $${2^{2m}}$$ elements, where $$\varphi $$ denotes Euler's totient function. This is a great improvement of previous results: for even m, the best known lower bound has been $$\frac{\varphi (m)}{2}\left( \lfloor \frac{m}{4}\rfloor +1\right) $$ ; for odd m, there has been no such lower bound at all. Moreover, we determine the automorphism group of Taniguchi's APN functions.
Nd and Hf isoscapes of the Yilgarn Craton, Western Australia and implications for its mineral systems

The Yilgarn Craton, Western Australia is endowed with significant gold, nickel‑copper, Cu-Pb-Zn volcanic-hosted massive sulphide (VHMS) and iron ore deposits. Detailed knowledge of the spatial relationships between mineral systems and the crustal framework of the Yilgarn Craton may be powerful tools in the predictive exploration of undiscovered mineral systems. Both Nd and Hf isotopes have the potential to image crustal residence, mantle tapping structures, and juvenile input over both space and time. This study uses a recent compilation of Hf (1236 zircon analyses) and Nd (462 whole rock analyses) isotopes to image the architecture of the crust and compare this to the distribution of gold, nickel‑copper-platinum group elements (Ni-Cu-PGEs), Cu-Pb-Zn VHMS and iron ore deposits. Spatial correlation plots reveal a statistically significant relationship between Nd and Hf isotopic domains and the occurrence of gold, Ni-Cu-PGE, base metals, and iron confirmed by the coefficient of determination (R2) which measures how differences in metal occurrences can be explained by differences in the isotopic signature. Nickel occurrences show the strongest antithetic spatial correlation (R2 = 0.80) with Hf model ages, followed by gold (R2 = 0.58) and base metals (R2 = 0.35), which implies higher metal endowment in areas of more juvenile crust. In contrast, there is a significant positive spatial correlation (R2 = 0.49) between iron ore occurrence and older model ages. Isotopic data can also be plotted as Hf or Nd time-slices, where the strongest spatial correlation between gold, nickel, and base metal occurrence and juvenile input occurs at ca. 2700–2550 Ma. Generally, a model can be proposed where (1) base metals are more prevalent in juvenile crust because of its thinner nature, greater mantle input, high-heat flow, increased water-magma interaction; (2) iron ore is concentrated in regions of old, mature, and stable crust; (3) komatiite-hosted Ni-Cu-PGE systems are focused in juvenile rift zones at the edge of old crustal blocks; and (4) gold is also localised in juvenile crust at craton edges where several factors are localised, including orogenesis, occurrence of previously fertilised crust (i.e. Fe-rich lithologies, sanukitoids), favourable structures (potential inversion of rift structures), and ‘late’ alkaline magmatism. In gold, Ni-Cu-PGE, and base metal mineral systems, the ability of magmas to access higher crustal levels unimpeded appears vital in regional prospectivity and is itself a function of crustal configuration.
Deep Over-the-Air Computation

As an efficient data fusion method, over-the-air computation integrates computation and communication by exploiting the superposition property of multiple access channels. In this paper, a framework on deep learning enabled over-the-air computation is proposed, where both the pre-processing and post-processing functions are represented by deep neural networks (DNNs). In this way, the over-the-air computation can approximate any function via learning through the data. The deep over-the-air framework is useful to a variety of machine learning applications on the Internet-of-Things (IoT). The experiments on distribution regression and anomaly detection have shown the effectiveness of the proposed method.
Design of an Enhanced Reconfigurable Chaotic Oscillator using G<sup>4</sup>FET-NDR Based Discrete Map

In this paper, a novel chaotic map is introduced using a voltage controlled negative differential resistance (NDR) circuit composed of an n-channel and a p-channel silicon-on-insulator (SOI) four-gate transistor (G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> FET). The multiple gates of the G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> FET are leveraged to create a discrete chaotic map with three bifurcation parameters. The three tunable parameters are the gain of a transimpedance amplifier (TIA), top-gate voltage of n- channel G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> FET, and top-gate voltage of p-channel G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> FET. Two methods are proposed for building chaotic oscillators using this discrete map. The effect of altering bifurcation parameters on chaotic operation is illustrated using bifurcation diagrams and Lyapunov exponent. A design methodology for building flexible and reconfigurable logic gate is outlined and the consequent enhancement in functionality space caused by the existence of three independent bifurcation parameters is demonstrated and compared with previous work.
A unified framework to learn program semantics with graph neural networks

Program semantics learning is a vital problem in various AI for SE applications e.g., clone detection, code summarization. Learning to represent programs with Graph Neural Networks (GNNs) has achieved state-of-the-art performance in many applications e.g., vulnerability identification, type inference. However, currently, there is a lack of a unified framework with GNNs for distinct applications. Furthermore, most existing GNN-based approaches ignore global relations with nodes, limiting the model to learn rich semantics. In this paper, we propose a unified framework to construct two types of graphs to capture rich code semantics for various SE applications.
A Kubernetes CI/CD Pipeline with Asylo as a Trusted Execution Environment Abstraction Framework

Modern commercial software development organizations frequently prescribe to a development and deployment pattern for releases known as continuous integration / continuous deployment (CI/CD). Kubernetes, a cluster-based distributed application platform, is often used to implement this pattern. While the abstract concept is fairly well understood, CI/CD implementations vary widely. Resources are scattered across on-premise and cloud-based services, and systems may not be fully automated. Additionally, while a development pipeline may aim to ensure the security of the finished artifact, said artifact may not be protected from outside observers or cloud providers during execution. This paper describes a complete CI/CD pipeline running on Kubernetes that addresses four gaps in existing implementations. First, the pipeline supports strong separation-of-duties, partitioning development, security, and operations (i.e., DevSecOps) roles. Second, automation reduces the need for a human interface. Third, resources are scoped to a Kubernetes cluster for portability across environments (e.g., public cloud providers). Fourth, deployment artifacts are secured with Asylo, a development framework for trusted execution environments (TEEs).
A semi-supervised deep convolutional framework for signet ring cell detection

Early detection of signet ring cell carcinoma (SRCC) can significantly improve patient survival rate. Pathological image analysis is applied as the golden standard for SRCC diagnosis. Automatic detection of pathological cells with deep learning methods can greatly reduce the burden of pathologists. Deep learning methods are commonly trained using large amounts of annotated data. However, due to the uneven distribution of medical resources and tedious manual examination procedure of high-resolution images, annotation data are usually insufficient and incomplete for deep learning model training. In this paper, we propose a new semi-supervised deep convolutional framework to address the data annotation problem for signet ring cell detection. Specifically, we propose a self-training strategy to generate pseudo bounding boxes based on Test Time Augmentation and modified Non-Maximum Suppression to re-train our detector. Our framework achieves 0.8774 in Valid Recall and 100.00 in FPs, winning the 1st place in the signet ring cell detection task of the Digestive-System Pathological Detection and Segmentation Challenge 2019. Code has been made publicly available at: https://github.com/ooooverflow/DigestPath2019.
The automatic recognition of ceramics from only one photo: The ArchAIDE app

Pottery is of fundamental importance for understanding archaeological contexts. However, recognition of ceramics is still a manual, time-consuming activity, reliant on analogue catalogues created by specialists, held in archives and libraries. The ArchAIDE project worked to streamline, optimise, and economise the mundane aspects of these processes, using the latest automatic image recognition technology, while retaining key decision points necessary to create trusted results. The project has developed two complementary machine-learning tools to propose identifications based on images captured on site. One method relies on the shape of the fracture outline of a sherd; the other is based on decorative features. For the outline-identification tool, a novel deep-learning architecture was employed, integrating shape information from points along the inner and outer surfaces. The decoration classifier is based on relatively standard architectures used in image recognition. In both cases, training the classifiers required tackling challenges that arise when working with real-world archaeological data: the paucity of labelled data; extreme imbalance between instances of the different categories; and the need to avoid neglecting rare types and to take note of minute distinguishing features of some forms. The scarcity of training data was overcome by using synthetically-produced virtual potsherds and by employing multiple data-augmentation techniques. A novel way of training loss allowed us to overcome the problems caused by under-populated classes and non-homogeneous distribution of discriminative features.
Proactive Autoscaling for Cloud-Native Applications using Machine Learning

Cloud computing and cloud-native applications have become standards for new developments in most organizations. Many companies are moving their workload toward microservices and profit from the cloud-native paradigm. However, there are still many challenges to overcome to optimize the Quality of Service using autoscaling and resource dimensioning. Autoscaling is defined as the capacity of cloud infrastructure and applications to resize themselves by adjusting the resource pool. Simple autoscaling can be achieved by a feedback-loop based on current workload resource utilization. Called reactive autoscaling, this technique may introduce inconsistency between workload and resource utilization. In this paper, we propose a proactive autoscaling framework using a learning-based forecast model to dynamically adjust the resource pool, horizontally (number of replicas) and vertically (resources pool). This framework uses a proactive autoscaling algorithm based on Long Short-Term Memory (LSTM) to improve the end-to-end latency for cloud-native applications. We developed a proof of concept to demonstrate this framework and the effectiveness of our proactive algorithm.
Artemis: An Improved Smart Contract Verification Tool for Vulnerability Detection

Smart contracts are programs running on top of the blockchain platform. With smart contract, users can use code to define arbitrary rules to manage their assets. However, the security vulnerabilities within those smart contracts have caused significant financial losses to their users. Existing smart contract verification tools based on symbolic execution are still not comprehensive in terms of the types of vulnerabilities detected. In this work, we present Artemis, an improved smart contract verification tool that can detect vulnerabilities that includes greedy, block information dependency, gasless send, and dangerous delegatecall. We have thoroughly evaluated our tool on 12899 smart contracts in terms of vulnerability detection effectiveness and efficiency. The experiment results show that the Artemis tool is precise and cost-effective for practical use.
An investigation of domain adaptation in speaker embedding space for speaker recognition

Speaker recognition continues to grow as a research challenge in the field with expanded application in commercial, forensic, educational and general speech technology interfaces. However, challenges remain, especially for naturalistic audio streams including recordings with mismatch between train and test data (i.e., when train or system development data and enrollment/test data or application data are collected from different sources). Mismatch conditions (Hansen and Hasan, 2015) can be divided into two categories, extrinsic (channel, noise, etc.) and intrinsic (duration, language, and speaker traits including stress, emotion, Lombard effect, vocal effort, accent). Here, we investigate speaker recognition for the domain mismatch problem (intrinsic mismatch) especially for those challenges introduced by NIST (National Institute of Standards and Technology) SRE (speaker recognition evaluation) in 2016 and 2018. The challenges introduced in NIST SRE-16 and SRE-18 include language mismatch between train (used for the development of the system) and enrollment/test (used at the application phase). Here, we develop three alternative speaker embedding systems; i-vector, t-vector (an improved triplet loss solution), and x-vector. In addition, a number of unsupervised and supervised (using pseudo labels) methods are also studied for domain mismatch compensation, especially applied at the back-end level. These include adapted PLDA, adapted discriminant analysis, as well as score normalization and calibration methods using unlabeled in-domain data. We propose new variations to discriminant analysis with support vectors (SVDA) as well. These results confirm that SVDA can measurably improve speaker recognition performance for SRE-16 and SRE-18 tasks respectively by +15% and +8% in terms of min-Cprimary; and for EER the gains are +14% and +16% respectively, using i-vector speaker embeddings as the baseline. These advancements offer promising steps toward addressing speaker recognition in naturalistic audio streams.
Cox-nnet v2.0: improved neural-network-based survival prediction extended to large-scale EMR data

Cox-nnet is a neural-network-based prognosis prediction method, originally applied to genomics data. Here, we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale population data, including those electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. When applied on a kidney transplantation dataset, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32-folds (n =10 000) and achieves better prediction accuracy than Cox-PH (P<0.05). It also achieves similarly superior performance on a publicly available SUPPORT data (n=8000). The high efficiency and accuracy make Cox-nnet v2.0 a desirable method for survival prediction in large-scale EMR data.Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0.Supplementary data are available at Bioinformatics online.