Reviewerly/exHarmony huggingface dataset:
A Communist Theory of Writing: Virno, Lyotard, and a Rewriting of the General Intellect

This chapter rewrites the general intellect in order to formulate a communist theory of writing. Such a theory is not concerned with the content of writing, but rather the character of the act of rewriting, one that is patient and occurs along a general line between intellect and stupidity, speech and silence, and knowledge and thought. This theory emerges from several rewritings of the general intellect. The chapter begins with Jean-François Lyotard’s theory of rewriting—which distinguishes between rewriting as remembering and rewriting as working through—and introduces the role of digital technologies in rewriting. I then turn to Virno’s rewriting of the general intellect, which expands the concept beyond determinate capabilities and knowledges, yet at the same time compresses it into a fundamental and linguistic truth that lacks an antagonism to capital. In the remainder of the chapter, I rewrite the general intellect again, returning it to its indeterminacy by positing a general and postdigital (squiggly) line between intellect and its other.
Classification of Mars Lineament and Non-Lineament Structure Based on ResNet50

This article will give a brief introduction about training ResNet to classify the non-lineament and lineament structure on Mars surface, which is specific referring to crater and lineament structure which are dorsum and vallis. We build a dataset containing 300 samples of each category and splitting them into training set and test whose proportion is 4:1. After the network structure and parameters optimization, it accuracy is able to reach around 85% (for crater), 92% (for dorsum) and 80% (for vallis) respectively.
A Smart User Authentication Approach using Sensing Seat

The support of smart technologies has greatly contributed to the emergence of smart office with various personalized services and applications. User authentication is one of the critical technologies enabling sectional services according to individual demands. This paper proposed a smart approach to identifying users based on a sensing chair, which is convenient and intuitive without extra actions from users. Our proposed method shows an overall classification accuracy of 92.2 % in a 33 subjects experiment using KNN algorithm, which uses a set of designed features to present the sitting pressure distribution. We also investigated the effects of density of uniform sensor distribution on user authentication accuracy. The original sensor placement (1024 uniformly-distributed sensors) was reduced and evaluated accordingly. The results indicated that a reasonably acceptable performance can be achieved using much fewer sensors (an overall classification accuracy of 87.7% with 64 uniformly-distributed sensors).
Significance of the Organization of E-Learning Management System in a Modern University

The article considers the advantages of distance learning as the most effective and least expensive method of training during the economic crisis. The introduction of E-learning (EL) in the educational process provides new opportunities for the growth of the global education market, provides everyone with the necessary knowledge, and develops digital literacy competencies. The authors consider measures taken by the Russian government to apply Internet technologies in the education, as well as targets set for a priority project aimed at the development of online education. The analysis of the degree to which IT technology penetrates the major segments of the education market in Russia is provided. The article summarizes some theoretical issues of LMS Moodle application both on the positive and negative sides, and offers practical suggestions for the qualitative integration of e-learning in the process of teaching and learning in the higher education institute on the basis of effective e-learning management and good management. The system of e-learning management is developed on the modeling method, the main purpose of which is to apply modern electronic technologies in the educational process, to prepare the methodological base of electronic educational resources, and to improve electronic educational technologies in the e-learning format.
Development of an algorithm for optimizing the technological process of kaolin enrichment

An algorithm has been developed to optimize the technological process of kaolin enrichment, based on the fuzzy sets theory; it allows evaluating the objects according to the grade of membership in a certain class and the gradient method. The dependences of the gradient values and the redistribution of the weights on initial values, as well as on the degree of correlation of the distributions of these weights, are considered in the paper.
An accurate low-light object detection method based on pyramid networks

Low light object detection is a challenging problem in the field of computer vision and multimedia. Most available object detection methods are not accurate enough in low light conditions. The main idea of low light object detection is to add an image enhancement preprocessing module before the detection network. However, the traditional image enhancement algorithms may cause color loss, and the recent deep learning methods tend to take up too many computing resources. These methods are not suitable for low light object detection. We propose an accurate low light object detection method based on pyramid networks. A low-resolution pyramid enhancing light network is adopted to lessen computing and memory consumption. A super-resolution network based on attention mechanism is designed before Efficientdet to improve the detection accuracy. Experiments on the10K RAW-RGB low light image dataset show the effectiveness of the proposed method.
A Secure Document Archive Implemented using Multiple Encryption

Documents are something that provides information or can be a certification for someone or also can be a legal report. The theft of such important documents/certificates can interfere/hamper an individual or organization from performing their work efficiently and can also lead to loss of possessions. In this manuscript, “Pocket Certificates” - a conventional document archive with the capability of securely storing those documents is presented. The solution is the use of a Double Encryption system based on an amalgamation of AES and 3DES standards. Use of enhanced security can be thought of as a compromise in reliability and smooth function of a system but to the process, there are some constraints to be set so that the Encryption/Decryption process does not hamper the usability. This paper takes account of such attributes and keeps a balance between all of them. There is also the use of other Hashing techniques like bycrypt securely storing user login details and use of passport middleware for unique user authentication requirements at each application stage. The paper consists of an overall web application for the secure archive to documents or important data.
Hybrid Chain And Dependability

The hybrid chain is proposed in this paper to investigate on a new blockchain network that is to be built across private and main nets, namely, a hybrid chain. Note that the hybrid chain is distinguished from the on/off-balanced chain such that the hybrid chain is across two different (e.g., Hypercubes private net vs. Ethereum main net) while the on/off-balanced chain across on-chain and off-chain (e.g., Ethereum main net vs. cloud). It is essential to build a dependable interface in between private and main nets if business to consumer (or vice versa) transactions are demanded for instance. In the course of interfacing across private and main nets, dependability is to be considered as one of the most critical design factors in order to ensure private transactions stay within the private territory and publicized transactions stay public in the main net, and further in order to facilitate a seamless yet dependable execution of transactions across the border. In this context, the efficacy of the privacy of the private net side and the publicity of the main net side will be addressed and modeled by tracing a transaction's stochastic process at a steady state. A protype for an isolated testing across the Ethereum and Hypercubes open source for validation purpose is proposed along with extensive parametric simulations.
Combating the Threats of Cybercrimes in Russia

Since the introduction of computer technologies and the internet, Russia has been trying to adopt different strategies on maintaining social order in cyberspace. The purpose of this article is, by studying the stages of enacting legislation against cybercrime, to explore the Russian model of cyberspace regulation. In order to get control over the internet and to maintain security and stability in society, the Russian government has implemented new provisions of the Criminal Code of the Russian Federation and other means. This article demonstrates how criminal law and other measures may be used to fight cybercrime, and how the legislative body reacts on social concern about cybercrime. Russian legislation against cybercrime passed the same phases as cyber law in other countries: first, criminalizing and penalizing particular cybercrime types, and then, developing a complicated system of cyber regulation. The process of cyber regulation was influenced by changing cybercrime characteristics, transformation of enforcement policy, and international treaties ratified by the Russian Federation. Regardless of active efforts in fighting cybercrime, Russian hackers are still a big threat to Russia and, in general, globally. The article concludes that the hacker subculture is one of the main factors producing cybercrime.
Research on Visual Programming System of Gesture Interaction Interface in VR

The emergence of virtual Reality(VR) has brought tremendous challenges to user interface(UI) design. The lack of a systematic summary of VR environment characteristics and UI models prevents us from developing mature design tools. This article intends to build a development system for VR interface based on gesture interaction and visual programming. Derived from a analysis of interface characteristics and interaction features in three-dimensional(3D) environment, an efficient UI development workflow is formed. This workflow can conveniently develop and debug the 3D interface in VR. The exploration of VR interface design methods in this article has important reference value for interface design in future.
SMS Spam Filtering Using Machine Learning Technique

Sending and receiving SMS is very ordinary thing for any individual’s daily life. But when at the moment, we receive undesirable SMS frequently that waste our time and money as well and consequently this moment gives us unpleasant feeling. If undesirable messages are to be sent to a huge volume of recipients erratically have resulted in displeasure by consumers but gives large profit to spammers. There are lots of reasons like high internet speed, very cheap smart phones and user friendly interface of mobile web and mobile applications that attracts a huge volume of mobile phone users. These are the key factors expected to shape the future of the market. This paper focuses on SMS Spam filtering techniques and compared their performance. We compared the machine learning model’s performance and finally result indicates that the Logistic Regression model performed well with accuracy reaching up to 96.59%.
Interactive Exploration of Compact Sequence Diagrams - JIVE based approaches

Sequence diagrams are a popular visualization notation to refine and describe use case scenarios for object oriented application development. They are used to model/specify object-to-object interactions in clear sequence. Tools such as JIVE can be used to re-construct back the scenario from a real program execution in the form of sequence diagram. The sequence diagrams thus obtained tend to be large and hence JIVE provides novel options to compact the visualization structure without distorting the semantics. In this paper, an interactive technique to explore compact sequence diagrams that allows users to expand and examine the compacted portions on demand is presented. At the heart of the technique is the call tree implementation that forms the back-end of the visualization. The call tree is extended to work with the sequence diagram at the front-end in the symbiotic manner enabling interactive exploration. The utility of the technique is demonstrated through experiments with a variety of programs.
Visilant: Visual Support for the Exploration and Analytical Process Tracking in Criminal Investigations

The daily routine of criminal investigators consists of a thorough analysis of highly complex and heterogeneous data of crime cases. Such data can consist of case descriptions, testimonies, criminal networks, spatial and temporal information, and virtually any other data that is relevant for the case. Criminal investigators work under heavy time pressure to analyze the data for relationships, propose and verify several hypotheses, and derive conclusions, while the data can be incomplete or inconsistent and is changed and updated throughout the investigation, as new findings are added to the case. Based on a four-year intense collaboration with criminalists, we present a conceptual design for a visual tool supporting the investigation workflow and Visilant, a web-based tool for the exploration and analysis of criminal data guided by the proposed design. Visilant aims to support namely the exploratory part of the investigation pipeline, from case overview, through exploration and hypothesis generation, to the case presentation. Visilant tracks the reasoning process and as the data is changing, it informs investigators which hypotheses are affected by the data change and should be revised. The tool was evaluated by senior criminology experts within two sessions and their feedback is summarized in the paper. Additional supplementary material contains the technical details and exemplary case study.
Light Pre-Trained Chinese Language Model for NLP Tasks

We present the results of shared-task 1 held in the 2020 Conference on Natural Language Processing and Chinese Computing (NLPCC): Light Pre-Trained Chinese Language Model for NLP tasks. This shared-task examines the performance of light language models on four common NLP tasks: Text Classification, Named Entity Recognition, Anaphora Resolution and Machine Reading Comprehension. To make sure that the models are light-weight, we put restrictions and requirements on the number of parameters and inference speed of the participating models. In total, 30 teams registered our tasks. Each submission was evaluated through our online benchmark system (https://www.cluebenchmarks.com/nlpcc2020.html), with the average score over the four tasks as the final score. Various ideas and frameworks were explored by the participants, including data enhancement, knowledge distillation and quantization. The best model achieved an average score of 75.949, which was very close to BERT-base (76.460). We believe this shared-task highlights the potential of light-weight models and calls for further research on the development and exploration of light-weight models.
Design and Implementation of System Which Efficiently Retrieve Useful Data for Detection of Dementia Disease

To analyze Hadoop techniques like MapReduce, which will help to process the data faster and in efficient way to detect dementia. For given voluminous dementia dataset, current solution uses different data partitioning strategies which experiences large communication cost and expensive mining process due to duplicate and unnecessary transactions transferred among computing nodes. To clear this issues proposed algorithm uses data partitioning techniques such as Min-Hash and Locality Sensitive Hashing which will reduce processing time and improve efficiency of final result. We are taking help of MapReduce programming model of Hadoop []. We implement this technique on a Hadoop platform. For pattern matching we use FPgrowth algorithm. Finally we shows that the proposed system requires less time to finding frequent item sets. The idea behind research is to adopt to cope with the special requirement of health domain related with patients.
An embedded system to measure ground-based solar irradiance signal

This paper describes the construction, design, and testing of a low-cost intelligent system to measure ground-based global solar irradiance signal. The prime characteristics of this developed system are accuracy, portability, minimize directional response, regulated internal temperature, protection from environmental hazards, remote monitoring, and finally, the cost which is much lower than the conventional pyranometer. The sensor module is designed using multiple sensors to achieve better directional response. The sensor module also has an electro-mechanical system to protect from different environmental hazards. The monitoring station has a graphical end with soft buttons for controlling the functionality of sensor station through a reliable wireless channel. The developed system tries to minimize the traditional shortcomings associated with solar radiation measurement system and offers mean absolute error of 1.27 W/m2 and average directional response error of 6.76%. This system can be utilized in solar radiation measurement applications where cost is a key factor for selection.
Design and performance of two decomposition paradigms in forecasting daily solar radiation with evolutionary polynomial regression: wavelet transform versus ensemble empirical mode decomposition

Forecasting daily solar radiation (DSR) is a critical concern while implementing solar energy harnessing projects, energy utility, and stakeholder decisions. In developing forecast models based on a data-driven technique, challenges are encountered by virtue of the chaotic nature of meteorological data that can exhibit a high degree of irregularities, and other forms of stochastic behavior, thus influencing the accuracy of the forecasted DSR. In this paper, we aim to investigate the predictability of DSR at two locations: Busan and Seoul (South Korea) and address data preprocessing issues by applying well-established decomposition techniques: wavelet transform and ensemble EMD (EEMD) to first preprocess the predictor data, where an evolutionary polynomial regression (EPR) algorithm is then used to forecast the DSR values. For wavelet-based (W) cases, there are three hybrid models, namely W-H-EPR (i.e., EPR algorithm integrated with Haar mother wavelet), W-D-EPR (i.e., EPR integrated with Dmay mother wavelet), and W-C-EPR (i.e., EPR integrated with Coiflets mother wavelet), while for the EEMD case, one hybrid model denoted as EEMD-EPR was constructed. All forecast models were benchmarked against a standalone, nondecomposed EPR model. In terms of the validation stage, EEMD-EPR with, respectively, 6.25% and 6.19% smaller values of RMSE and MAE in comparison with the W-H-EPR model stood at the first rank at Seoul station. Additionally, in the case of Busan station, this technique with roughly higher NSE (1.88%) and WI (0.51%) had the highest accuracy before W-C-EPR. Finally, an uncertainty analysis was performed for assessing the capacity of developed EEMD-EPR and compared with those of existing predictive approaches, showing robust estimation accuracy in favor of proposed models. The evaluations of statistical indices revealed that the EPR model integrated with EEMD procedure is acceptable as a robust tool for DSR forecasting.
LiDAR Imaging-based Attentive Perception

In this paper we present a novel approach on attentive robotic perception by developing a saliency model on LiDAR imaging. Modern LIDAR sensors provide access to a multitude of structured images, namely intensity, reflectivity, ambient, and range. These images can in turn be fused and a saliency model can be developed in a manner analogous to the human attentive system but tailored to the uniqueness of LiDAR perception. The derived LiDAR-based saliency model exploits a bottom-up approach according to which the reflectivity, intensity, range, and ambient images are compared to each other and through a sequence of image processing steps multiple conspicuity maps are acquired which in turn give rise to a unified saliency map that efficiently encodes which objects are most important and worth further analysis. This attentive perception system on LiDAR imaging gives rise to efficient obstacle detection for robotic systems that provides 360deg coverage and allows millisecond-order execution times on embedded processors. The derived model is experimentally evaluated on a set of datasets from ground and flying robots.
Video Games to Support Language Therapies in Children with Hearing Disabilities

SATReLO is a tool for the development of personalized applications, supporting language therapy in children with hearing disabilities. Applications consist of mini-games that replicate some therapeutic activities. These applications would motivate children to embrace therapy positively, increasing the time they would dedicate towards this therapy, especially at home; therefore, accelerating the therapeutic process. SATReLO allows the therapist to adapt the activities carried out according to the needs of each patient while keeping a record of each child's evolution over time. Preliminary tests show that children interact easily with mini-games and are enthusiastic about using them.
Learning Multi-scale Retinex with Residual Network for Low-Light Image Enhancement

Existing network-based techniques addressing low-light image enhancement are developed by using the complex network architectures. However, the performance is not ideal, and they cannot give a reasonable interpretation of the effects of each layer in the network. To settle these issues, we design a residual network to learn multi-scale Retinex for handling low-light image enhancement. To be concrete, inspired by multi-scale Retinex, we define a new residual-type multi-scale Retinex model to gradually remove the illumination generated by the convolutional procedure. Thanks to the progressive mechanism, we can build an intuitive and explicit relationship between our residual-type multi-scale Retinex model and residual network. This enables us can directly utilize the residual network to learn a residual-type multi-scale Retinex by integrating the data distribution. Precisely because of our transparent modeling procedure, we can recognize the effects of each layer in our learnable architecture. It is valuable for more effectively exploit the network layers to handle this task. Extensive analytical experiments are performed to verify the effectiveness of our proposed method. A series of evaluative experiments are conducted to illustrate our superiority against other state-of-the-art methods.
Object-Based Goal Recognition Using Real-World Data

Goal and plan recognition of daily living activities has attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities based on multiple steps performed by human beings in an environment. In this work, we address the problem of plan and goal recognition of human activities in an indoor environment. Unlike existing approaches that use only actions to identify the goal, we use objects and their relations to identify the plan and goal towards which the subject in the video is pursuing. Our approach combines state-of-the-art object and relationship detection to analyze raw video data with a goal recognition algorithm to identify the subject’s ultimate goal in the video. Experiments show that our approach identifies cooking activities in a kitchen scenario.