Reviewerly/exHarmony huggingface dataset:
A Summary of Research on Web Data Acquisition Methods Based on Distributed Crawler

Under the background of big data, data mining and full utilization have become necessary ways for enterprises to improve their competitiveness. How to quickly and accurately obtain the data that meets the needs has become the key to data mining. At present, the main method of data acquisition is web crawlers. In order to improve the performance of the crawler system and achieve accurate and efficient data acquisition, this article tracks and studies the latest technical methods and introduces distributed web crawler technology. Based on this, the distributed crawler methods and crawler strategies based on Scrapy-Redis, cloud platform, and Nutch are analyzed. The results show that the use of distributed crawlers has obvious advantages in obtaining large-scale Web data. In order to compare the effect of distributed crawlers on large-scale data acquisition, verification experiments of three distributed crawler methods were carried out. The experimental results confirmed the effectiveness of distributed crawler technology in Web data acquisition.
On the Resilience of Even-Mansour to Invariant Permutations

Abstract Symmetric cryptographic primitives are often exposed to invariances: deterministic relations between plaintexts and ciphertexts that propagate through the primitive. Recent invariant subspace attacks have shown that these can be a serious issue. One way to mitigate invariant subspace attacks is at the primitive level, namely by proper use of round constants (Beierle et al., CRYPTO 2017). In this work, we investigate how to thwart invariance exploitation at the mode level, namely by assuring that a mode never evaluates its underlying primitive under any invariance. We first formalize the use of invariant cryptographic permutations from a security perspective, and analyze the Even-Mansour block cipher construction. We further demonstrate how the model composes, and apply it to the keyed sponge construction. The security analyses exactly pinpoint how the presence of linear invariances affects the bounds compared with analyses in the random permutation model. As such, they give an exact indication how invariances can be exploited. From a practical side, we apply the derived security bounds to the case where the Even-Mansour construction is instantiated with the 512-bit ChaCha permutation, and derive a distinguishing attack against Even-Mansour-ChaCha in $$2^{128}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>128</mml:mn> </mml:msup> </mml:math> queries, faster than the birthday bound. Comparable results are derived for instantiation using the 200-bit Keccak permutation without round constants (attack in $$2^{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>50</mml:mn> </mml:msup> </mml:math> queries), the 1024-bit CubeHash permutation (attack in $$2^{256}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>256</mml:mn> </mml:msup> </mml:math> queries), and the 384-bit Gimli permutation without round constants (attack in $$2^{96}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>96</mml:mn> </mml:msup> </mml:math> queries). The attacks do not invalidate the security of the permutations themselves, but rather they demonstrate the tightness of our bounds and confirm that care should be taken when employing a cryptographic primitive that has nontrivial linear invariances.
SeRoT: A Secure Runtime System on Trusted Execution Environments

Trusted execution environment (TEE) is a promising technique to protect user programs and data on public cloud environments. To support unmodified applications running, many TEE runtime systems have been proposed. However, a major drawback of the existing schemes is the lack of interface protection. This problem may lead to many security problems, such as memory information leakage and malicious codes attacks. To tackle this problem, we propose SeRoT, a new secure runtime system on trusted execution environments. Our secure runtime system first provides some core functions to the enclave programs. Then we protect the host interface at two levels, binary interface level and application interface level. In these two levels, we prevent the adversary interfacing with malicious messages. Furthermore, we implement SeRoT on a RISC-V based platform and show our scheme is average about 10% faster than Keystone on two popular and representative benchmarks.
Quantum-synergetic ontology of generalized corporeality (II): post-nonclassics, temporality, and reflection of acting

In this article, the author advances the idea of generalized corporeality in its functional manifestations in the acts of communication, activity, and theatrical art. The work is dedicated to the problem of &amp;ldquo;smart body&amp;rdquo;, in other words, conscious and manageable generalized corporeality is one of the core themes in development and self-improvement of acting skills and onstage genuineness. The key problem is the problem of harmony, unity between inner and outside, words and doings, thoughts and actions, realized by the actor just partially, so is by the stage director and audience. The author attempts to describe this problem by means of post-nonclassical philosophical methodology offered by V. S. Stepin, concepts of post-nonclassical practices, synergy of motion and state, which marks the synthesis of the ontologies of states of generalized corporeality and temporal ontologies, introduced in the previous article. The author also attracts the concept of unconscious reflection of V. Lefebvre, which describes a special type of reflection as a certain unconscious component of generalized corporeality that creates a specific interpretation of the skill and work with time in post-nonclassical context. Interpretation of the acquired acting skills that taught in many schools of classical and contemporary theatre, from K. Stanislavski and M. Chekhov to A. Artaud and E. Barba, are also viewed through the prism of reflection of synthetic ontologies of generalized corporeality. In conclusion, the author introduces theatrical interpretation of the twofold activity triad, which allows embracing the relationship between various observers, the actors of theatrical event: actor &amp;ndash; stage director &amp;ndash; audience.
SAR Image Super-Resolution Base on Weighted Dense Connected Convolutional Network

In this paper, a weighted dense connected convolutional network(WDCCN) is proposed for SAR image super-resolution. In the network, to enhance feature propagation and the super-resolution performance, each layer will receive the output from all the previous layers in a different weight proportion. At last, the experimental results indicate that weighted dense connected convolutional network can realize SAR image super-resolution well.
JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Prediction

Analysis of online reviews has attracted great attention with broad applications. Often times, the textual reviews are coupled with the numerical ratings in the data. In this work, we propose a probabilistic model to accommodate both textual reviews and overall ratings with consideration of their intrinsic connection for a joint sentiment-topic prediction. The key of the proposed method is to develop a unified generative model where the topic modeling is constructed based on review texts and the sentiment prediction is obtained by combining review texts and overall ratings. The inference of model parameters are obtained by an efficient Gibbs sampling procedure. The proposed method can enhance the prediction accuracy of review data and achieve an effective detection of interpretable topics and sentiments. The merits of the proposed method are elaborated by the case study from Amazon datasets and simulation studies.
Analytical framework for social media risk analysis in organizations

In this paper we examine the complex and changing configurations of social media risks in organizations. To gain a deeper understanding of the nature, structure and interrelatedness of social media risks we develop a framework for risk analysis that provides multifaceted profiles of risk types and risk incidents. Through the process of developing the risk analysis framework key insights into social media risks in organizations were made visible. Social media risks were found to be: i) evolving, overlapping and highly interrelated leading to derivative risks and risk chains; ii) spatiotemporal in nature, having broad spatial reach, both geographical and relationally and long temporal frames that link current risks to future consequences, emphasizing the need for a longitudinal view iii) representing multiple stakeholder interests and risk appetites, and iv) blurring the boundaries between business, professional and individual risks and perceptions of risk. These insights open up imperatives for further research and invite the potential for alternative theoretical lenses to examine these variations in perceptions and spatiotemporal perspectives on social media risk.
SNAP: A Communication Efficient Distributed Machine Learning Framework for Edge Computing

More and more applications learn from the data collected by the edge devices. Conventional learning methods, such as gathering all the raw data to train an ultimate model in a centralized way, or training a target model in a distributed manner under the parameter server framework, suffer a high communication cost. In this paper, we design Select Neighbors and Parameters (SNAP), a communication efficient distributed machine learning framework, to mitigate the communication cost. A distinct feature of SNAP is that the edge servers act as peers to each other. Specifically, in SNAP, every edge server hosts a copy of the global model, trains it with the local data, and periodically updates the local parameters based on the weighted sum of the parameters from its neighbors (i.e., peers) only (i.e., without pulling the parameters from all other edge servers). Different from most of the previous works on consensus optimization in which the weight matrix to update parameter values is predefined, we propose a scheme to optimize the weight matrix based on the network topology, and hence the convergence rate can be improved. Another key idea in SNAP is that only the parameters which have been changed significantly since the last iteration will be sent to the neighbors. Both theoretical analysis and simulations show that SNAP can achieve the same accuracy performance as the centralized training method. Compared to the state-of-the-art communication-aware distributed learning scheme TernGrad, SNAP incurs a significantly lower (99.6% lower) communication cost.
Bitcoin Data Analytics: Scalable techniques for transaction clustering and embedding generation

Bitcoin provides pseudo-anonymity to its users, leading to many transactions related to illicit activities. The advent of mixing services like OnionBC, Bitcoin Fog, and Blockchain.info has allowed users to increase their anonymity further. This paper tackles the pseudo-anonymity of the Bitcoin blockchain by developing a scalable spark based framework to find patterns in the transaction data. The efficacy of the framework is demonstrated by performing exploratory analysis. Furthermore, the paper shows the capabilities of bitcoin-based graph representations and addresses the issue of user profiling based on unsupervised learning approaches for analysing Bitcoin transactions and users. The authors convert the transaction graph of the Bitcoin data to contain only Wallet-IDs and generate graph embeddings using Variational Graph Autoencoder [1]. Additionally, the authors use explainable-AI techniques and Kohonen self organizing maps to visualize and understand the results obtained from the unsupervised learning methods.
Sparse Representation for Sentiment Analysis

Sentiment analysis has been attracted many attentions in recent years. Various studies have been done with the main focus on feature representation and classification. Term Frequency-Inverse Document Frequency is the most common statistical feature which has been used in the literature that extracts high dimensional sparse features.In this paper, we propose to reduce data dimensionality by considering the assumption that high dimensional sentiment data lies on some low dimensional manifolds. This assumption is confirmed by numerous studies on high dimensional data and text mining. Spectral embedding is applied for dimensionality reduction. Since the performance of spectral embedding is heavily dependent on the graph of representing manifold(s), it is proposed to enforce sparse representation for generating suitable graph. The proposed method is compared with common dimensionality reduction methods in sentiment analysis on different classifiers and the results demonstrate that it significantly outperforms other methods.
System Development Management E-School as a Students Information Media

Abstract The Filial e-school website of LPKA Class I Palembang has indeed provided supporting learning materials for students for formal education at elementary, junior high, and high-school levels. However, entrepreneurship-materials are not yet available on the website as an effort to equip the future of students after leaving LPKA is very important to be improved. The development of e-school Filial LPKA was carried out by presenting entrepreneurial materials interactively. The method used in developing entrepreneurship learning multimedia video tools is the Multimedia Development Life Cycle (MDLC) method. Entrepreneurship-material is presented in a multimedia video format that can be accessed by students at a booth in the LPKA environment. The booth provided is equipped with a set of multimedia personal computers that are connected to the internet network. Each student is given their respective account by the LPKA to be able to access entrepreneurial-material video content on the e-school website with the URL address:https://e-schoolfiliallpka.com. Every student who accesses this e-school website, can interact directly with the system provided, for example by asking questions about the entrepreneurial video material being watched and questions will be answered by practitioners/contributors, students can also express their interest by giving likes. LPKA can also monitor student visit charts for each video material they watch and graphics of student favorite video material. The LPKA party can find out the area of interest of each student that is available so that this effort is expected to increase the motivation and ability of their entrepreneurs in the future. The e-school website also provides a virtual online gallery space for students’ work so that the products of entrepreneurship while at LPKA can be exhibited to the public so that the wider community can find out and visit online and in this way is expected to increase their confidence in the work.
Research on image super-resolution based on attention mechanism and multi-scale

Abstract In order to solve the problem of the single feature scale of the generated image in the SISR field and the lack of texture information, a parallel generation confrontation network structure based on the attention mechanism and multi-scale is proposed on the basis of SRGAN, which adopts a dual generator and discriminator combined with attention module model. Train the network to learn multi-scale features, and integrate high-frequency information of different scales in the residual network. The experimental results on Set5, Set14, and BSD100 benchmark data sets prove that the algorithm has a good effect in restoring image detail information.
A Privacy-Preserving Data Collection and Processing Framework for Third-Party UAV Services

Unmanned Aerial Vehicles (UAVs) are becoming more popular than ever in outdoor commercial services. Many third-party UAV companies offer their UAVs as mobile data collectors to assist their clients in remote data collection missions. However, due to the lack of trust and transparency, the clients often have very little control on the behavior of these UAVs. This issue is even exacerbated if the service had to deal with private client data. In this work, we propose a solution that enables third-party UAVs to collect and process private client data from remote data sites in a trustworthy and efficient manner. We design and implement the Secure Homomorphic Encryption (SHE) framework. SHE combines trusted hardware enclave and homomorphic encryption technologies to provide strong privacy primitives on client data. SHE features in a recrypt technique such that the computation and communication overhead for homomorphic encryption on the client data is minimized. In addition, SHE takes the advantage of UAVs' travelling time to run data aggregation tasks in order to speed-up data processing. Through laboratory experiments, we demonstrate that SHE can meet the performance requirement in many common data processing and aggregation missions. SHE thus can be introduced as a trustworthy framework for the third-party UAV service providers.
An Image Classification Model that Learns MNIST Image Features and Numerical Information

In this study, we focused on the differences between image features and meta-information. For example, "0" and "9" are relatively similar in terms of image characteristics, but there is significant difference in terms of the numbers they actually mean. In contrast, "3" and "4" are relatively dissimilar in terms of image features, but the difference is small in terms of the values they actually mean. Therefore, we propose a method to learn not only the features of the image but also the actual meaning of the values. As a result, we were able to reduce the error.
Sentiment Analysis and Opinion Mining Using Deep Learning for the Reviews on Google Play

Sentiment analysis and opinion mining have an important role to trace consumer behavior. With the recent advances in machine learning techniques, this issue has been addressed and come a long way in English. However, in agglutinative languages such as Turkish, it has been still one of the hot topics. In this study, we compare several classification methods and deep learning methods to make a sentiment analysis for Turkish reviews that we have collected from Google Play. We have 87.30% prediction accuracy for multinomial Naive Bayes and 95.87% prediction accuracy for deep learning model. We have significant results both from the machine learning classifiers and the deep learning model. While there is no difference between machine learning classifiers when we use different vectorizers, there is a difference when we build a deep learning model to predict target value. This model can also be applied to any data from Twitter, Facebook, or any other microblogging.
Automatic Chain Line Segmentation in Historical Prints

The analysis of chain line patterns in historical prints can provide valuable information about the origin of the paper. For this task, we propose a method to automatically detect chain lines in transmitted light images of prints from the 16th century. As motifs and writing on the paper partially occlude the paper structure, we utilize a convolutional neural network in combination with further postprocessing steps to segment and parametrize the chain lines. We compare the number of parametrized lines, as well as the distances between them, with reference lines and values. Our proposed method is an effective method showing a low error of less than 1 mm in comparison to the manually measured chain line distances.
Translation of Interactive Datalog Programs for Microcontrollers to Finite State Machines

“Smart” devices have become ubiquitous in modern households and industry. Especially in home-automation, robotics, and sensing tasks, rule-based approaches seem ideal to describe the behavior of the interactive systems. But managing input data and state is hard. With little choice of programming language, most code targeted at microcontrollers is written in imperative C or assembler. Microlog is a deductive database language with a strong logic foundation based on Datalog extended with a representation of time and calls to external functions that may be used to control sensors and actors. In this paper we describe a method to precalculate sets of Datalog facts that may be derivable for a point in time. Values that will be known only at runtime are represented as parameters of those “states”. During “state transition”, a small number of conditions on parameters and input values must be checked. By representing a possibly quite large number of facts as a single state number and a few parameter values, memory and computing time are saved. If no parameters are needed, the result of this compilation is basically a finite state machine.
Collision Risk Assessment via Awareness Estimation Toward Robotic Attendant

With the aim of contributing to the development of a robotic attendant system, this study proposes the concept of assessing the risk of collision using awareness estimation. The proposed approach enables an attendant robot to assess a person's risk of colliding with an obstacle by estimating whether he/she is aware of it based on behavior, and to take the requisite preventative action. To implement the proposed concept, we design a model that can simultaneously estimate a person's awareness of obstacles and predict his/her trajectory based on a convolutional neural network. When trained on a dataset of collision-related behaviors generated from people trajectory datasets, the model can detect objects of which the person is not aware and with which he/she at risk of colliding. The proposed method was evaluated in an empirical environment, and the results verified its effectiveness.
Neoproterozoic copper-gold mineralization in the Amani area, southwestern Tanzania

The Amani area in southwestern Tanzania contains several prospects where primary Cu-Au mineralization occurs at surface, the most noteworthy of these being the Amani Hills and Mpanga Hill prospects. At the Amani Hills prospect, structurally-controlled gold mineralization is hosted in quartz-muscovite schists of the Amani Group and minor high-grade gneisses belonging to the Kabelege Group. Here, quartz-carbonate veins contain pyrite-pyrrhotite-chalcopyrite, with free-milling gold occurring predominantly in quartz and along sulphide grain boundaries. These veins are associated with sericite-epidote-albite alteration of the wallrocks. At the Mpanga Hill prospect, Cu ± Au mineralization is hosted within impure micaceous marbles of the Rafiki Group. At this prospect, both the sulphidised carbonate host rocks and quartz-carbonate veins contain chalcopyrite, bornite and pyrite assemblages, with accessory phases of Au ± Pb ± Zn ± Ag ± Bi ± Se. Fluid inclusion microthermometry indicate that auriferous veins at both prospects record a bimodal occurrence of early low-temperature and low-salinity (≤23.2 wt% NaCl eq.) fluids that are overprinted by later, high temperature and hypersaline fluids (32.8 – 54 wt% NaCl eq.). These ore-forming fluids contain H2O-NaCl ± CO2 ± CH4 compositions and were trapped at near-lithostatic to supralithostatic conditions of 330 – 400 °C and 1.7 – 6.9 kbar. These hydrothermal fluids were likely variably sourced and were focussed along kilometre-scale brittle-ductile reverse shear zones in the Amani area, where fluid-rock interactions led to Cu-Au mineralization. 40Ar/39Ar dating of muscovite constrained the timing of copper – gold mineralization in the Amani area to between c. 550 – 600 Ma. Mineralization is therefore related to the Pan-African aged Kuunga orogeny, which resulted from the collision between the Tanzania Craton and Bangweulu Block during the assembly of supercontinent Gondwana. The new findings presented in this study highlight the exploration potential of copper – gold mineralization along large-scale Proterozoic shear zones in the underexplored and overlooked Upangwa terrane of southwestern Tanzania.