Reviewerly/exHarmony huggingface dataset:
Improving Resource Isolation of Critical Tasks in a Workload

Typical cluster schedulers co-locate critical tasks and background batch tasks to improve the utilization of resources in the cluster. However, this leads to resource contention and interference between the diverse co-located tasks. To ensure guaranteed resource allocation and predictability, critical tasks are executed within containers as they provide resource isolation using container resource allocation mechanisms. Linux-based containers achieve resource allocation and isolation using a kernel feature known as Control Groups (cgroups). Cgroups allow the division of CPU time into shares which can be allocated to different groups of tasks. In our study, we run workloads on servers with different hardware configurations and measure the CPU time per second, or the CPU bandwidth, that the critical tasks in the workloads can consume. Our workloads have been generated using a cluster trace published by Google, and contain a mixture of critical and background tasks. The results of the experiments show that under high CPU load conditions, the CPU bandwidth consumed by the critical tasks is inadequate and unstable because of the poor resource isolation offered by cgroups. However, when these tasks are scheduled with the careful use of SCHED_DEADLINE policy, which is based on the Global Earliest Deadline First and Constant Bandwidth Server algorithms, they steadily consume their required CPU bandwidth irrespective of the load on the CPU. As a result, when critical tasks are scheduled using SCHED_DEADLINE, they experience 3 $$\times $$ –40 $$\times $$ smaller delays than under cgroups.
A blockchain-based technological solution to ensure data transparency of the wood supply chain

The transparency of the product data registration in a supply chain is a key factor in the forestry sector that settles the origin of the wood. This problem can be supported by various emerging technologies, such as blockchain, a shared and immutable ledger that facilitates the process of recording transactions and tracking assets in a commercial network. This provides the authorities with an increase in data integrity, making it more difficult for fraud or changes to records, documents, and authorizations. For this reason, blockchain technology would help to combat illegal logging by applying the benefits of transparency in transactions and records, ensuring the integrity of information. Our project aims to implement a technological solution based on cloud blockchain to reduce illegal logging in Peru, preventing internal or external users from violating or altering the registration of records, trees, and certificates.
Open innovation knowledge management in transition to market economy: integrating dynamic capability and institutional theory

This study provides a theoretical framework and empirical evidence to argue that a knowledge management process under the open innovation paradigm brings a viable solution for firms, especially those in transition economies, to acquire valuable knowledge-based dynamic capabilities to respond to environmental changes and achieve desirable organizational performance.These knowledge-based capabilities in turn enable firms to enhance their economic performance in terms of productivity and profitability.Dynamic capabilities act as an intermediary that bridges firms' open innovation efforts and their economic realization.Local institutional quality plays an important moderating role in this process.Micro-sized firms have not consistently obtained the expected economic benefits from their open innovation efforts, which requires more policy attention.For empirical evidence, we consider a comprehensive range of measures for open innovation and dynamic capabilities.Our proposed hypotheses are tested in a set of seemingly unrelated equations by combining two datasets from the Vietnam SME survey and the Provincial Competitiveness Index survey.As a robustness check, we estimate the performance equation applying fixed effect regression and one-year lag structure.
Breast Cancer Classification Using Transfer Learning

Cancer is one of the most lethal forms of the disease. And in females, breast cancer is the most common cancer which could even lead to death if not properly diagnosed. Over the years, a lot of advancement can be seen in the field of medical technology but when it comes to detecting breast cancer biopsy is the only way. Pathologists detect cancer by using histological images under the microscope. Inspecting cancer visually is a critical task; it requires a lot of attention, skill and is time-consuming. Therefore, there is a need for a faster and efficient system for detecting breast cancer. Advancements in the field of machine learning and image processing lead to multiple types of research for creating an efficient partially or fully computer monitored diagnosis system. In this paper, we have used histological images to detect and classify invasive ductal carcinoma. Our approach involves convolutional neural networks which are a very advanced and efficient technique when dealing with images in machine learning. We compared various famous deep learning models, and we used these pre-trained CNN architectures with fine-tuning to provide an efficient solution. We also used image augmentation to further improve the efficiency of the solution. In this study, we used VGG, ResNet, DenseNet, MobileNet, EfficientNet. The best result we got was using fine-tuned VGG19 and with proper image augmentation. We achieved a sensitivity of 93.05% and a precision of 94.46 with the mentioned architecture. We improved the F-Score of the latest researches by 10.2%. We have achieved an accuracy of 86.97% using a pre-trained DenseNet model which is greater than the latest researches that achieved 85.41% [30] accuracy.
A Survey of Temporal Activity Localization via Language in Untrimmed Videos

Video is one of the most informative media which consists of visual, textual and audio contents. As the number of videos on the Internet grows explosively, it is increasingly necessary for machines to understand the semantic information in the videos accurately. Temporally Activity Localization in a video is such a work which needs to localize the video moment that is most semantically similar to a given natural query. This task is quite challenging for that it not only requires to have a deep understanding of the sentences and videos, but also the fine-grained interactions between the two modalities. In this paper, we report a comprehensive survey of existed temporal sentence localization techniques. Firstly, we make a detailed classification and analysis of these methods. Then we discuss the experimental results and performance of existed approaches. Finally, we present some insights for future research direction.
Multi-face detection algorithm suitable for video surveillance

With the continuous improvement of urban construction, people's demand for social security has gradually increased. It is an effective method to identify criminals by using face detection technology equipped with city monitoring. In places with dense crowd flow, multiple faces of different sizes will appear, and the face detection algorithm cannot take into account both the detection speed and the detection accuracy. In this paper, by using data augmentation method, redesigning the detection network structure with deep separable convolution, and optimizing the NMS algorithm,the improved MTCNN algorithm is faster than MTCNN algorithm in the application environment of processing multi-face detection .The accuracy rate reaches 92%. At the same time, the output of mtcnn on face qualiy is increased to strengthen the robustness of face detection and improve the recognition accuracy for subsequent face recognition.
Evaluating the Need and Effect of an Audience in a Virtual Reality Presentation Training Tool

Public speaking is an essential skill in everyone’s professional or academic career. Nevertheless, honing this skill is often tricky because training in front of a mirror does not give feedback or inspire the same anxiety as presenting in front of an audience. Further, most people do not always have access to the place where the presentation will happen. In this research, we developed a Virtual Reality (VR) environment to assist in improving people’s presentation skills. Our system uses 3D scanned people to create more realistic scenarios. We conducted a study with twelve participants who had no prior experience with VR. We validated our virtual environment by analyzing whether it was preferred to no VR system and accepted regardless of the existence of a virtual audience. Our results show that users overwhelmingly prefer to use the VR system as a tool to help them improve their public speaking skills than training in an empty environment. However, the preference for an audience is mixed.
Prediction of iron content in soil based on microspectrophotometry analysis

Soil is a very important type of trace evidence. The iron content of soil is of great significance in distinguishing soil types, discriminating among different soils, and tracing soils. However, conventional methods for analyzing the iron content of soil are expensive, laborious, and time-consuming. Previous studies have shown that the color of soil correlates well with its hematite content. This article thus deals with the indirect determination of iron content using soil color as a proxy. Soil color measurements were conducted using microspectrophotometry (MSP), and resulting data were transformed into chromaticity value (L*, a*, and b*). Predictions using the redness index in conjunction with a linear regression model were compared with those using the chromaticity value and a back propagation neural network (BPNN) model. The influences of different modeling conditions on the modeling accuracy were compared, and more accurate predictions were achieved when the iron content was higher than 2.13%. The BPNN model produced predictions with R2 and RMSE values of 0.955 and 0.336%, which were better than the predictions of the linear regression model (R2: 0.859, RMSE, 1.07%). We thus demonstrated that MSP can be used for fast, accurate, and non-destructive measurements of soil color and prediction of its iron content. Although the results may not be as precise as conventional laboratory analysis, they still provide more information with acceptable accuracy, which should have promising applications in forensic applications.
Application of AI Identification Technology in Foreign Language Education

The advantages of artificial intelligence in the field of language education are prominent. Voice recognition technology facilitates the judgment of spoken language and oral testing. Image recognition technology can greatly reduce the workload of teachers in correcting composition and translation, and it can also provide learners with diagnostic Feedback; Emotion recognition technology enables teachers to better understand the emotional state of students and take corresponding adjustment or interaction with students. It can also provide feedback to the main robots and make them take various encouraging measures according to different situations.
Gated temporal convolutional neural network and expert features for diagnosing and explaining physiological time series: A case study on heart rates

Background and Objective: Physiological time series are common data sources in many health applications. Mining data from physiological time series is crucial for promoting healthy living and reducing governmental medical expenditure. Recently, research and applications of deep learning methods on physiological time series have developed rapidly because such data can be continuously recorded by smart wristbands or smartwatches. However, existing deep learning methods suffer from excessive model complexity and a lack of explanation. This paper aims to handle these issues. Methods: We propose TEG-net, which is a novel deep learning method for accurately diagnosing and explaining physiological time series. TEG-net constructs T-net (a multi-scale bi-directional temporal convolutional neural network) to model physiological time series directly, E-net (personalized linear model) to model expert features extracted from physiological time series, and G-net (gating neural network) to combine T-net and E-net for diagnosis. The combination of T-net and E-net through G-net improves diagnosis accuracy and E-net can be utilized for explanation. Results: Experimental results demonstrate that TEG-net outperforms the second-best baseline by 13.68% in terms of area under the receiver operating characteristic curve and 11.49% in terms of area under the precision-recall curve. Additionally, intuitive justifications can be provided to explain model predictions. Conclusions: This paper develops an ensemble method to combine expert features and deep learning method for modeling physiological time series. Improvements in diagnostic accuracy and explanation make TEG-net applicable to many real-world health applications.
High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images

Generating photorealistic images of human faces at scale remains a prohibitively difficult task using computer graphics approaches. This is because these require the simulation of light to be photorealistic, which in turn requires physically accurate modelling of geometry, materials, and light sources, for both the head and the surrounding scene. Non-photorealistic renders however are increasingly easy to produce. In contrast to computer graphics approaches, generative models learned from more readily available 2D image data have been shown to produce samples of human faces that are hard to distinguish from real data. The process of learning usually corresponds to a loss of control over the shape and appearance of the generated images. For instance, even simple disentangling tasks such as modifying the hair independently of the face, which is trivial to accomplish in a computer graphics approach, remains an open research question. In this work, we propose an algorithm that matches a non-photorealistic, synthetically generated image to a latent vector of a pretrained StyleGAN2 model which, in turn, maps the vector to a photorealistic image of a person of the same pose, expression, hair, and lighting. In contrast to most previous work, we require no synthetic training data. To the best of our knowledge, this is the first algorithm of its kind to work at a resolution of 1K and represents a significant leap forward in visual realism.
Evaluating MFCC-based speaker identification systems with data envelopment analysis

The concept of the efficiency of speaker recognition systems varies in the literature. Although many authors have defined efficiency as recognition accuracy, others have defined it as low energy consumption, memory storage, or computational burden. In our study, for a novel approach, speaker recognition was evaluated following a multi-criteria decision-making approach in two stages. First, speaker identification based on Mel-frequency cepstrum coefficients (MFCC) was conducted for various parameters and methods, including number of speakers, number of MFCCs, test speech duration, training utterance length and the various classifiers. Classification metrics, memory storage, testing, and training time of the trials were measured as well, and the performance of the trials was examined for each criterion. Verifying the literature, the study revealed that no parameters or methods achieved the best performance for all criteria. In the second stage, a multi-criteria efficiency analysis, as suggested in the literature, was conducted according to various application scenarios. By using data envelopment analysis, the efficiency of trials according to the scenarios was determined. After ranking the efficiency scores, it was revealed that the best solution was task-dependent. From the perspective of classifiers, artificial neural networks outperformed the others considering benefits to cost; however, some of their costs were high, whereas the other classifiers provided the best solutions in light of cost criteria. Last, the number of MFCCs was the least effective parameter for efficiency. Altogether, the findings indicate that the efficiency of a speaker identification system cannot be defined as recognition accuracy, memory storage, testing time or training time but as a function of those criteria.
CCA-Secure (Puncturable) KEMs from Encryption with Non-Negligible Decryption Errors

Public-key encryption (PKE) schemes or key-encapsulation mechanisms (KEMs) are fundamental cryptographic building blocks to realize secure communication protocols. There are several known transformations that generically turn weakly secure schemes into strongly (i.e., IND-CCA) secure ones. While most of these transformations require the weakly secure scheme to provide perfect correctness, Hofheinz, Hövelmanns, and Kiltz (HHK) (TCC 2017) have recently shown that variants of the Fujisaki-Okamoto (FO) transform can work with schemes that have negligible correctness error in the (quantum) random oracle model (QROM). Many recent schemes in the NIST post-quantum competition (PQC) use variants of these transformations. Some of their CPA-secure versions even have a non-negligible correctness error and so the techniques of HHK cannot be applied. In this work, we study the setting of generically transforming PKE schemes with potentially large, i.e., non-negligible, correctness error to ones having negligible correctness error. While there have been previous treatments in an asymptotic setting by Dwork et al. (EUROCRYPT 2004), our goal is to come up with practically efficient compilers in a concrete setting and apply them in two different contexts: firstly, we show how to generically transform weakly secure deterministic or randomized PKEs into CCA-secure KEMs in the (Q)ROM using variants of HHK. This applies to essentially all candidates to the NIST PQC based on lattices and codes with non-negligible error, for which we provide an extensive analysis. We thereby show that it improves some of the code-based candidates. Secondly, we study puncturable KEMs in terms of the Bloom Filter KEM (BFKEM) proposed by Derler et al. (EUROCRYPT 2018) which inherently have a non-negligible correctness error. BFKEMs are a building block to construct fully forward-secret zero round-trip time (0-RTT) key-exchange protocols. In particular, we show how to achieve the first post-quantum secure BFKEM generically from lattices and codes by applying our techniques to identity-based encryption (IBE) schemes with (non-)negligible correctness error.
A Multi-Model Ensemble Approach for Gold Mineral Prospectivity Mapping: A Case Study on the Beishan Region, Western China

Mineral prospectivity mapping (MPM) needs robust predictive techniques so that the target zones of mineral deposits can be accurately delineated at a specific location. Although an individual machine learning algorithm has been successfully applied, it remains a challenge because of the complicated non-linear relations between prospecting factors and deposits. Ensemble learning methods were efficiently applied for their excellent generalization, but their potential has not been fully explored in MPM. In this study, three well-known machine learning models, namely random forest (RF), support vector machine (SVM), and the maximum entropy model (MaxEnt), were fused into ensembles (i.e., RF–SVM, RF–MaxEnt, SVM–MaxEnt, RF–SVM–MaxEnt) to produce a final prediction. The paper aims to investigate the potential application of stacking ensemble learning methods (SELM) for MPM. In this study, 69 hydrothermal gold deposits were split into two parts: 70% for the training model and 30% for testing the model. Then, 11 mineral prospecting factors were selected as a spatial dataset constructed for MPM. Finally, the models’ performance was assessed using the receiver operating characteristic (ROC) curves and five statistical metrics. Compared with other single methods, the SELM framework showed an improved predictive performance in the model evaluation. Therefore, this finding suggests that the SELM framework is promising and should be selected as an alternative technique for MPM.
Disentangled Information Bottleneck

The information bottleneck (IB) method is a technique for extracting information that is relevant for predicting the target random variable from the source random variable, which is typically implemented by optimizing the IB Lagrangian that balances the compression and prediction terms. However, the IB Lagrangian is hard to optimize, and multiple trials for tuning values of Lagrangian multiplier are required. Moreover, we show that the prediction performance strictly decreases as the compression gets stronger during optimizing the IB Lagrangian. In this paper, we implement the IB method from the perspective of supervised disentangling. Specifically, we introduce Disentangled Information Bottleneck (DisenIB) that is consistent on compressing source maximally without target prediction performance loss (maximum compression). Theoretical and experimental results demonstrate that our method is consistent on maximum compression, and performs well in terms of generalization, robustness to adversarial attack, out-of-distribution detection, and supervised disentangling.
Insect Inspired View Based Navigation Exploiting Temporal Information

Visual navigation is a key capability for robots. There is a family of insect-inspired algorithms that use panoramic images encountered during a training route to derive directional information from regions around the training route and thus subsequently visually navigate. As these algorithms do not incorporate information about the temporal order of training images, we describe one way this could be done to highlight this information’s utility. We benchmark our algorithms in a simulation of a real world environment and show that incorporating temporal information improves performance and reduces algorithmic complexity.
Distant studying experience reflection during pandemic of COVID-19 (on the example of teaching in the technical university)

Abstract Changing over distant teaching during the pandemic COVID-19 is considered as pedagogical experiment. Conditions and methods of privet teacher’s experiment carrying out on the bases of humanitarian subject teaching in the technical university are described: the lists of pedagogical and informational and communicative technologies used on lessons before pandemic and during high-alert mode. Comparison of students’ lessons attendance is made when different formats are used, using possibilities of the feedback, interaction with each other, peculiarities of doing tasks are analyzed. Advantages and disadvantages of online lectures and practical lessons are analyzed according to the teachers’ and students’ point of view. In the article’s conclusion the ascent is made up on the personal senses lost analyses and personal knowledge transmission impossibility using distant methods strictly.
PhishTrim: Fast and adaptive phishing detection based on deep representation learning

Phishing is a kind of network attack which is famous for stealing users' private information without their knowledge. Although researchers have proposed many phishing detection methods, most methods are computationally expensive and difficult to update their detection rules based on changes in attack patterns. In this paper, we propose PhishTrim, a lightweight phishing URLs detection method based on deep representation learning, which is fast and adaptive. We get the initial embedding representation of the URLs through the Skip-gram pre-training model. Bidirectional Long Short Term Memory (Bi-LSTM) is then used to extract context dependency to further learn the deep representation of URLs. The local n-gram features are extracted using Convolutional Neural Networks (CNN). Experiments show that PhishTrim performs better on large-scale datasets with 99.797% accuracy, and indicate that our method has a certain ability to detect zero-day phishing attacks. We have published our PhishTrim2019 dataset at https://github.com/DataReleased/PhishTrim.