Reviewerly/exHarmony huggingface dataset:
A Feature-Flux Traffic Camouflage Method based on Twin Gaussian Process

Recent work has shown that the properties of network traffic may reveal some patterns (such as, payload size, packet interval, etc.) that can expose users' identities and their private information. The existing defense approaches, such as traffic morphing, protocol tunneling, still suffer from revealing the special traffic pattern. To address this problem, we propose a feature-flux traffic camouflage method (FFTC). FFTC forecasts the pattern of normal traffic via twin Gaussian process(TGP), and dynamically change the on-going traffic feature based on the learned traffic pattern to conceal the camouflaged traffic in the normal traffic. TGP-based traffic forecasting makes FFTC more sensitive to the feature dynamics of normal traffic. Then, the camouflaged traffic can always synchronize with the normal traffic pattern in real time. Furthermore, FFTC can learn multiple traffic patterns from different kinds of normal traffic, and dynamically change the camouflaged traffic pattern to achieve the feature-flux ability. From the experimental results, FFTC improves the indistinguishability of the camouflaged traffic and the normal traffic, and the feature-flux of the camouflaged traffic mitigates the traffic analysis attack effectively.
Estimating the Predictive Accuracy of a Classifier

This chapter is concerned with estimating the performance of a classifier (of any kind). Three methods are described for estimating a classifier’s predictive accuracy. The first of these is to divide the data available into a training set used for generating the classifier and a test set used for evaluating its performance. The other methods are $k$-fold cross-validation and its extreme form $N$-fold (or leave-one-out) cross-validation. A statistical measure of the accuracy of an estimate formed using any of these methods, known as standard error is introduced. Experiments to estimate the predictive accuracy of the classifiers generated for various datasets are described, including datasets with missing attribute values. Finally a tabular way of presenting classifier performance information called a confusion matrix is introduced, together with the notion of true and false positive and negative classifications.
Fixed-point Quantization for Vision Transformer

Recently, transformer-based models has shown promising results on miscellaneous computer vision tasks. However, its high computation cost makes it neither practical to deploy on mobile devices, nor economic to compute on servers. In this paper, we propose two effective quantization schemes for reducing the memory usage and computation consumption of vision transformers. First, we develop an approximation-based Post-training Quantization (PTQ) approach which optimizes for a set of quantization scaling factors that minimize quantization errors. Moreover, we introduce a learning-based Quantization-aware Training (QAT) approach that allows for model finetuning after inserting quantization operations to restore accuracy. Furthermore, we reveal the complementary effects of learning-based approach and approximation-based approach in QAT and propose an effective strategy for the initialization of quantization parameters. We evaluate our approaches on ImageNet for different vision transformer models. Our quantization algorithms outperform the previous state-of-art approaches for both post-training quantization and quantization-aware training benchmark. With weights and activations in vision transformer quantized to 8-bit integers, we obtain a ×4 compression rate of model parameters with an accuracy drop of less than 0.2% for models of various scales.
Computer analysis of histopathological images for tumor grading. 2

We have upgraded our own original color filtration pixel-by-pixel (CFPP) method (Klonowski et al 2018a Physiol. Meas. 39 034002) to enable not only automatic and rapid assessment of the proliferation index of a tumor or neoplasm but also quick automatic location of hot-spots (regions of interest, ROIs) in immunohistochemically stained microscopic images of neoplasms and tumors.Neoplastic cells stain differently from normal cells. By counting in a given window the number of pixels belonging to the given subspaces of (R,G,B) color space which correspond, respectively, to proliferating cells (which are mostly neoplastic) and non-proliferating cells (which are mostly normal) we calculate the local proliferation index in this window. The window is moved all around the whole histopathological virtual slide (WSI) or around a chosen part of the WSI. By adding the respective numbers calculated for all the windows covering the WSI or the chosen part of it one can easily calculate the global proliferation index.The method is rapid and does not require the time-consuming step of selecting ROIs manually nor does it need computationally complicated detection of hot-spots, both of which attempt to emulate a pathologist's way of thinking. We apply our method to a set of slide images of diffuse large B-cell lymphoma.By appropriate changes in the (R,G,B) color filtration thresholds, our method may be adapted to the analysis of other types of tumors. It may also be adapted for analysis of microscopic images in neuropathology. Because of its rapidity and simplicity it may also used for analysis of series of images to assess local dynamics of image complexity in network physiology applications.
Evaluation of Machine Translation Quality Based on Neural Network and Its Application on Foreign Language Education

The form of communication at home and abroad requires translators to constantly develop new professional skills and language skills to meet the needs of translation, At present, neural network translation has become the mainstream of machine translation technology. Based on this, this paper analyzes the quality of machine translation based on neural network and its impact on foreign language education. Through the combination of questionnaire survey and experimental comparison, this paper studies the use and response of machine translation in students' daily learning through questionnaire survey the differences between traditional machine translation and neural network-based machine translation are analyzed based on the combination of questionnaire and experiment. From the data analysis, we can see that the accuracy and efficiency of neural network machine translation are improved. When the vocabulary is 500, the accuracy is increased by 8%, when the time is increased by 1 s, when the vocabulary is 1000, the accuracy is increased by 11%, when the time is increased by 2.3S, 1500 words The accuracy of convergence time increased by 9%, the time increased by 3.4s, the accuracy of 2000 vocabulary increased by 9%, and the time increased by 4.5s.
Experimental archaeology

Experimental archaeology involves examining craft techniques and the traces of usage on tools and materials, as well as retracing the correlations between events, techniques, and everyday life in the past. Experimental archaeology does so by conducting scientific, methodologically designed studies in which the size of the variables can be altered and empirical data and information can be gained. The conflict between inductive and deductive methods is evident in contemporary debates over experimental archaeology and some of its methodological approaches. The impetus for a new take on the experimental approach in archaeology came—as it had in the 19th century—from the archaeological open-air museums of Scandinavia. The concept and methods of experimental archaeology have finally been established, yet debates over the definition and scope of the field continue. European scholars differentiate between living history, reenactment, live action role-play, experimental archaeology, histotainment, and historical theatre.
Bridging Heuristic and Deep Learning Approaches to Sensor Tasking

Space is becoming a more crowded and contested domain, but the techniques used to task the sensors monitoring this environment have not significantly changed since the implementation of James Miller’s marginal analysis technique used in the Special Perturbations (SP) Tasker in 2007. Centralized tasker / scheduler approaches have used a Markov Decision Process (MDP) formulation, but myopic solutions fail to account for future states and non-myopic solutions tend to be computationally infeasible at scale. Linares and Furfaro proposed solving an MDP formulation of the Sensor Allocation Problem (SAP) using Deep Reinforcement Learning (DRL). DRL has been instrumental in solving many high-dimensional control problems previously considered too complex to solve at an expert level, including Go, Atari 2600, Dota 2, Starcraft 2 and autonomous driving. Linares and Furfaro showed DRL could converge on effective policies for sets of up to 300 objects in the same orbital plane. Jones expanded on that work to a full three-dimensional case with objects in diverse orbits. DRL methods can require significant training time to learn from an a priori state. This paper builds on past work by applying imitation learning to bootstrap DRL methods with existing heuristic solutions. We show that a Demonstration Guided DRL (DG-DRL) approach can effectively replicate a near-optimal tasker’s performance using trajectories from a sub-optimal heuristic. Further, we show that our approach avoids the poor initial performance typical of online DRL approaches. Code is available as an open source library at: https://github.com/AshHarvey/ssa-gym
Solar Spectra

Every living being on Earth is using either a direct form of energy from the sun i.e. sunlight, as in the case of plants, or a derived form as in the case of animals. Solar energy is, thus, the basis of all forms of life on Earth. In the present era for human beings, there is an ever increasing demand of energy for use in every walk of life e.g. industry, transport, technology driven gadgets, healthcare etc. The future livelihood of human beings demands an extensive use of solar energy by photovoltaics technology for sustainable development. The adoption of solar photovoltaic technology is ultimately dependent on the nature of sunlight that reaches the Earth. The sunlight reaching a location on the surface of the Earth is affected by (a) sun-earth geometry and (b) atmospheric effects. This chapter addresses the effects of Earth’s atmosphere on sunlight before it reaches the Earth at a given location. It gives the various spectra of sunlight, as received, for different times of the day and for different atmospheric and environmental conditions. It also discusses the reflection of sunlight by the ground surface, i.e. the so-called “albedo”. Finally, it gives some representative examples for the spectra of indoor lighting and other low-light sources.
HYBRID RECOMMENDATION APPROACH BASED ON A VOTING SYSTEM: EXPERIMENTATION IN AN EDUCATIONAL CONTEXT

We present in this paper the results of a second experiment that was recently conducted to evaluate a hybrid recommendation approach in an online learning environment.The approach consists of mixing the two approaches of content-based filtering and collaborative filtering to improve the relevance of the educational resources recommended to learners.A first experiment was carried out in 2019 and gave convincing results, which led us to repeat a second experimentation in order to confirm the results, on the one hand, and on the other hand, to modify the way learners evaluate the resources by transforming the "like" by a vote from one to five, in order to verify whether this will bring an improvement in the recommendations.This second experiment was also an opportunity to integrate an engine that guides learners' searches by adding criteria relating to their preferences and to check their satisfaction with the use of this engine.The results were globally positive.
DSM

The number of cores and the capacities of main memory in modern systems have been growing significantly. Specifically, memory scaling, although at a slower pace than computation scaling, provided opportunities for very large DRAMs with Terabytes (TBs) capacity. Consequently, addressing the performance and energy consumption bottlenecks of DRAMs is more important than ever. DRAM memory refresh operation is one of the main contributing factors to the memory overheads, especially for large capacity DRAMs used in modern servers and emerging large-scale data centers. This paper addresses the memory refresh problem by leveraging the fact that most cloud servers host virtualized systems that use similar kernels, libraries, etc. We propose and experimentally evaluate a novel approach that exploits this observation to address the DRAM refresh overhead in such systems. More specifically, in this work, we present DSM, a light-weight hardware extension in memory controller to detect the pages with same content in memory and refresh only one of them and redirect the requests to the others to this page. Our detailed experimental analysis shows that the proposed DSM design can reduce 99\textsuperscriptth percentile memory access latency by up to 2.01x, and it also reduces the overall memory energy consumption by up to 8.5%.
An Economical Methodology to Rhetorical Identifications in Cloud Victimization Virtual Machine Snapshots

Distributed computing is a rising innovation that is in effect generally embraced all through the world because of its usability. Associations of various types can utilize it without pre-requirements, for example, IT infra-structure, specialized abilities, administrative over-burden, stockpiling limit, preparing force, and information recuperation or protection setup. It can be profited by all customers according to their requirements, desires and spending plan. In any case, distributed computing present's new sorts of security vulnerabilities that should be promotion dressed. Customary “PC forensics” manages location, acquisition and counteractive action of IT activated fakes and violations, however, it does not have the capacity to manage cybercrimes relating to distributed computing condition. In this article, the authors concentrate on legal sciences issues in distributed computing, survey restrictions of criminological group and present the hindrances looked amid evaluation. As the basis of the cloud computing and the implementation in the cloud environment is a great task to protect the user information without causing any security issue and the consistency in the data must be provided by the service provider. Distributed systems or the operations in the distributed environment will increase the usability of the resources as well as the capability of the data transmission and provide the information required in an effective manner without interrupting the security issues. But even though the clients from the different parts of the globe are focusing on the gaps in security in the Cloud computing and distributed environment. Here we are focusing on the business model that will increase the revenue of the firms which are concentrating on implementing the cloud computing and the distributed environment in their respective areas. Forensics in the customer management in the distributed environment will give the complete picture on the digital marketing, standards of data distribution and the security. In this article we focus on the security implementation and the raise of utilization of the distributed environments and the cloud data storage capabilities. This will more focus on the data security.
Pedagogical conditions for forming and developing the competence of self-education and self-improvement of teachers

The article reveals and substantiates the pedagogical conditions for the formation of the competence of self-education and self-improvement of teachers in the educational space of postgraduate education institutions. The main research methods are theoretical and systemic analysis; systematization of information on the problem of the formation and development of the competence of self-education and self-improvement in the system of postgraduate education. The essence of the concept of "competence of self-education and self-improvement" of a teacher of higher education is revealed. It is proposed to consider the designated concept as the readiness and ability of an individual to carry out effective independently cognitive creative activity within a certain professional sphere due to the formation of self-learning knowledge and skills of self-educational activity. The article analyzes the experience of its formation among teachers during their training in the system of postgraduate education. An important pedagogical condition for the implementation of the process of formation and development of the competence of self-education and self-improvement of teachers is the willingness of scientific and pedagogical workers of the institution of postgraduate education to stimulate the applicant for education to self-education and self-improvement, which contributes to the development of his personality, the ability for independent personal and professional development in the future. It is stated that the second pedagogical condition is the provision of self-education and self-improvement of the teacher in the process of mastering the content of the advanced training course on the basis of the institution of postgraduate education. It was established by means of a survey of students of advanced training courses and interviews with scientific and pedagogical workers of IATPS NUPh that the specified pedagogical conditions for the formation and development of the competence of self-education and self-improvement of teachers provide for the creation of a stimulating educational environment, contributes to the inclusion of both teachers and applicants for the institution of postgraduate education in the process of creative activity and stimulates the development of their professional and personal qualities.
A FCN Approach to Blockage Correction in Radars

Doppler weather radar is the most widely used convection detector with the highest resolution in the ground. Echo reflectance data from the weather radar is the key reference for the meteorological department to carry out severe convective weather forecast and early warning, quantitative precipitation estimation(QPE) and quantitative precipitation forecast(QPF). However, in the process of radar detection, it is inevitable to be affected by obstacles, ground object echo interference, radar echo attenuation and other phenomena, resulting in poor data quality of detection results. Therefore, it is very important to correct the missing or disturbed data. On the other hand, with the rapid development of artificial intelligence technology in recent years, more and more meteorological researchers begin to introduce deep learning and other machine learning methods into the research of meteorological field such as weather radar data processing. In this paper, a deep convolutional encoder-decoder network is proposed to correct the beam blocking of weather radar. In this study, the correction of radar beam blockage is regarded as an image inpainting problem. It's the first trying to use deep learning to realize the correction of radar beam blockage. Experiment shows that the method proposed in this paper is significantly better than the traditional method in accuracy, error rate, false alarm rate and other aspects. The method can directly identify and correct the blocking area, and the operation procedure is simple compared traditional methods.
New Bio Inspired Techniques in the Filtering of Spam

The internet era promotes electronic commerce and facilitates access to many services. In today's digital society the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This paper deals on the unveiling of fresh bio-inspired techniques (artificial social cockroaches (ASC), artificial haemostasis system (AHS) and artificial heart lungs system (AHLS)) and their application for SPAM detection. For the authors' experimentation, they have used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy and error). They have optimising the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine learning algorithms (decision tree C4.5 and K-means).
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this chapter, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated. I submit that three alternative supervised learning methods – namely lasso penalties, bagging, and boosting – offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.
WITHDRAWN: Addressing the eye-fixation problem in gaze tracking for human computer interface using the Vestibulo-ocular Reflex.

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Creativity complicates tweets: a quantitative lens on syntactic characteristics of twitter

Abstract Twitter is a newly emerged computer-mediated discourse (CMD) genre in recent years for exchanging short textual messages with the 140-character constraint. Little explicitly quantitative attention has been given to the syntactic structure of twitter, though. Under the framework of dependency grammar, the present study intends to investigate the syntactic characteristics of twitter based on quantitative methods by a comparison with traditional written genres and CMD genres. Results show that (1) most tweets are syntactically easy to understand, shaped by the principle of least effort. With the shortest sentence length, twitter has more syntactically difficult sentences than do traditional fictions, which somehow contradicts the common belief. (2) The word order of twitter demonstrates a different distribution from that of the other genres. (3) Twitter’s distribution of dependency types in relation to all the other genres shows a dynamic property. Some of the frequencies of syntactic relations of twitter are similar to those of traditional fiction and others close to those of traditional news. To summarize, creative linguistic features of twitter interestingly complicate syntactic characteristics, through which we may obtain new insights regarding twitter syntax as well as possible contextual factors behind that.