Reviewerly/exHarmony huggingface dataset:
A Novel Approach on Mobile Devices Fast Authentication and Key Agreement

Mechanism to-Rube Goldberg invention accord is normal habituated to for apartment phones and Internet of Things. Agree and central knowledge are open to meet an unfailing turning between twosome gadgets. In ignoble fracas, factual methodologies many a time eon wait on a prefabricated solitarily pronunciation database and bear the ill effects of serene age rate. We verifiable GeneWave, a brusque gadget inspection and root assention convention for item cell phones. GeneWave mischievous accomplishes bidirectional ingenious inspection office on the physical reaction meantime between two gadgets. To evade the resolution of interim in compliance, we overshadow overseas time fragility on ware gadgets skim through steep flag location and excess time crossing out. At zigzag goal, we success out the elementary acoustic channel reaction for gadget verification. We combination an extraordinary coding pointing for virtual key assention while guaranteeing security. Consequently, two gadgets heart signal couple choice and safely concur on a symmetric key.
Anna Siyanova-Chanturia and Ana Pellicer-Sánchez (eds): UNDERSTANDING FORMULAIC LANGUAGE: A SECOND LANGUAGE ACQUISITION PERSPECTIVE

The role of formulaic language (FL) is now recognized as central in linguistic processing and performance, particularly in supporting speed and fluency. In the second language acquisition (SLA) context, attention has extended to corpus linguistics, teaching pedagogy, and psycholinguistics. While a clear set of conclusions has yet to fully emerge, there is a consensus that second language (L2) speakers would benefit from enhancing their use of FL. However, the reason why they do not seem to do so remains something of a mystery. As a researcher and TESOL teacher (and struggling L2 learner of Japanese), I welcome this volume as a source of the latest thinking on FL in the L2 context. While it is not the first to provide a state-of-the-art overview (others include Corrigan et al. 2009; Polio 2012), it is unusual in focusing entirely on the SLA perspective. In their introduction, the editors acknowledge the myriad ways that FL has been conceptualized, defined, and measured. Consistent with this, they define formulaicity broadly, so as to be maximally inclusive in the coverage of the contributing chapters.
Horacio Saggion, Automatic Text Simplification. Synthesis lectures on human language technologies, April 2017. 137 pages, ISBN:1627058680 9781627058681

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A Multi-objective Optimization Algorithm Based on Monarch Butterfly Optimization

Swarm intelligence optimization algorithm is an important technology that solves the complex optimization problem by simulating the behavior of biological groups in nature. Monarch butterfly optimization (MBO) algorithm is such a swarm intelligence algorithm that simulates the migration behavior of the monarch butterflies in nature. It has received great success on solving single-objective optimization problems, but few contributions on multi-objective problems. In this paper, we modify MBO to solve multi-objective problems, and then propose a new multi-objective optimization algorithm based by combining effective strategies from other swarm-based algorithms. A series of benchmark functions are employed to evaluate the performance of this algorithm. We compare the experimental results with three basic algorithms and state-of-the-art algorithms. It is shown that the proposed algorithm performs better than the compared algorithms on most of the benchmark functions.
Study of the classroom arrangement problem based on ant colony algorithm

Abstract In this paper, the ant colony algorithm is presented to optimize the university classroom arrangement and solve the corridor congestion problem. Firstly, the classroom capacity, the course time periods and the distance between classrooms are considered, and then the ant colony algorithm is applied to the corridor congestion problem by minimizing the distance between the students’ classroom of the courses. The experimental results show that the ant colony algorithm is able to construct feasible solutions and solve the classroom arrangement problem.
Anomaly Detection Algorithm Based On Electric Equipment

Abstract Traditional methods for detecting data anomalies of power equipment fail to fully mine the data characteristics. And it has shortcomings such as complex calculation, poor flexibility and low accuracy. To solve the problem of abnormal fault detection of electric equipment, the abnormal detection algorithm based on statistical analysis and machine learning is used in the paper. The reconstruction method based on Long Short Term Memory (LSTM) time sequence is proposed for time series data abnormal detection. Experiments show that the new method is effective in detecting and correcting abnormal data, which reduces the detection time and improves the accuracy of state estimation results. There are huge differences within the positive samples in anomaly detection, so several methods are studied in the paper. One-class SVM algorithm is often used in novelty detection and isolation forest algorithm is used in outlier detection. Machine learning methods which is based on statistical analysis will play an increasingly important role in the fields of equipment monitoring and predictive maintenance, safety of electric equipment.
Optimized Encoding Methods of the Overlapped Rectangular Non-symmetry and Anti-packing Model

The overlapped rectangular non-symmetry and anti-packing model (ORNAM) is a kind of image representation model developed from the original non-overlapped one. Aimed at the difference of them, this paper proposes two methods to improve the compression performance of the ORNAM coding (ORNAMC) for gray images. One is the adaptive arithmetic coding. The other is the adjacent position correlation encoding based on column bisections. In the experiment, we compare the improved algorithms with the original one and the classic quad tree coding algorithm. The results showed that our improvement increases the compression ratio under the premise of maintaining the quality of gray images.
A Method for Determining the Location of Unmanned Vehicles in Matched Maps

In this paper, a matchable road map is used to determine the specific location of unmanned vehicle in the road. Firstly, the matching map is established according to the characteristic obstacles of point cloud data processing. Then, the data collected by lidar are processed and matched with the matching map to determine the specific location of unmanned vehicle in the road. The experiment locates the coordinate points of 1705 positions. The accuracy of mid-positioning reaches 93.37%, which shows that the method is simple and reliable.
Fully-Online Suffix Tree and Directed Acyclic Word Graph Construction for Multiple Texts

We consider the construction of the suffix tree and the directed acyclic word graph (DAWG) indexing data structures for a collection $$\mathcal {T}$$ of texts, where a new symbol may be appended to any text in $$\mathcal {T} = \{T_1, \ldots , T_K\}$$, at any time. This fully-online scenario, which arises in dynamically indexing multi-sensor data, is a natural generalization of the long solved semi-online text indexing problem, where texts $$T_1, \ldots , T_{k}$$ are permanently fixed before the next text $$T_{k+1}$$ is processed for each k ($$1 \le k < K$$). We first show that a direct application of Weiner’s right-to-left online construction for the suffix tree of a single text to fully-online multiple texts requires superlinear time. This also means that Blumer et al.’s left-to-right online construction for the DAWG of a single text requires superlinear time in the fully-online setting. We then present our fully-online versions of these algorithms that run in $$O(N \log \sigma )$$ time and O(N) space, where N is the total length of the texts in $$\mathcal {T}$$ and $$\sigma $$ is their alphabet size. We then show how to extend Ukkonen’s left-to-right online suffix tree construction to fully-online multiple strings, with the aid of Weiner’s suffix tree for the reversed texts.
Relationship between S-and σ-inverse for some class of nonsquare matrices – an initial study

In this paper the new results concerning the relations between nonunique right/left inverses of nonsquare polynomial matrices are presented. A particular attention is focused on the relationship between the S - and σ-inverse both having the arbitrary so-called degrees of freedom. The proposed approaches allow to obtain essential analytical formulas, which constitute the starting point for further research effort.
Evaluating Image Blurring for Photographic Portraiture

Image blurring is a major source of image degradations that leads to loss of details. Detecting blurring region of a photo and evaluating the degree of blurring are crucial for image quality assessment. It’s more complicated for portraiture when the blurring sometimes is deliberate by photographer for visual effects. Aiming at evaluating the blurring metric of snapshot or amateur photos with faces, the paper proposed a simple and effective no-reference method of evaluating image blurring. The key idea is taking into account the artistic purpose on portraiture. The proposed method is based on the concept of Cumulative Probability of Blur Detection and pooling strategy. After computing the burring metric of pixels, the final value of global blurring evaluation is obtained with the pooling strategy according to the characteristic of human skin. Experimental results on public databases and photos collected from Internet show the proposed method can significantly improve the accuracy of objective blurring evaluation metric that have stronger correlation with subjective human assessment.
Improvement of a Sorting-Out GMDH Algorithm Using Recurrent Estimation of Model Parameters

The paper presents and discusses a GMDH algorithm MULTI-R being an improved and revised version of the known multistage algorithm MULTI based on the successive search of model of the globally optimal structure. This means that the MULTI algorithm is intended for discovering the result of exhaustive search by the combinatorial algorithm COMBI GMDH with radically less computations. But this algorithm has some substantial drawbacks, for example, it tends to choose underfitted models in the searching process and is not optimized with respect to the parameter estimation procedures. The new revised version MULTI-R differs from the original algorithm MULTI by using a recurrent procedure of parameters estimation and additional optimizing the model structure to enhance both the computation speed and accuracy of discovering the globally optimal model. The comparative numerical characteristics of the processing speed and structural accuracy of this modified algorithm and the original one are given for several test tasks.
User Friendly Department Assistant Robo

In this article we proposed a friendly interaction between human machines in the assistant department robot (DA_ROB). Our system is able to interact with people, provide information and act as an assistant to a department. The system has friendly interface and automatic speech friendly recognition. Friendly user interface takes a significant responsibility for direct interaction with people. The main aim behind the creation of the robot is to reduce the work of faculties. Bluetooth module is used to receive the voice signal and produce response. GSM is mainly used for communication. RFID reader is used to get the output for attendance purpose. Ultrasonic is used to measure distance and also provide the facility of security camera. It is based on machine learning and raspberry pi is used to implement the robot.
Why SQL Injection Attacks Are Still Plaguing Databases

This article describes how SQL injection has been a long-standing problem in database security. It is understandable why injection is considered number one because of the sheer number of web applications that exist currently. An injection attack can allow an attacker to gain complete access of a database which oftentimes contains sensitive information. This results in a loss of confidential information which places consumers at a huge risk.
Security Algorithms for Cloud Based Applications

The concept of cloud computing provides various scalable resources and dynamic capacity of storage which performs operations on the internet. The main benefit from using the cloud computing is the reduction in the economic expenditure and ease in accessibility of data. Data protection is also one of the major concern of cloud computing and in order to provide the security to it’s users, cloud computing uses different cryptographic encryption algorithm. The main concern of this paper is to analyze which encryption algorithm will be most efficient and convenient to be used in cloud computing to secure data as well as less time consuming algorithm. Analysis have been done on different criteria like block size, key length, security and speed, encryption flow chart and decryption flow chart.
Deliberate Exponential Chaotic Encryption Map

In the given work a new chaotic map is presented, referred to as deliberate exponential chaotic encryption map (DECM), the proposed map characteristics meets the recent demand of simple encryption algorithms with strong security. The methodology of iterative examination and correction was used to reach the optimal characteristics of the map. The chaotic behavior of the proposed map is examined, and its characteristics are proved experimentally, the achieved results shows advantages of the proposed map over the well-known chaotic maps. This gives the proposed map a potential to be implemented in real-time encryption algorithms for different kinds of information
Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems

In this study, we investigate how individual users' rating characteristics affect the user-level performance of recommendation algorithms.We measure users' rating characteristics from three perspectives: rating value, rating structure and neighborhood network embeddedness.We study how these three categories of measures influence the predictive accuracy of popular recommendation algorithms for each user.Our experiments use five real-world datasets with varying characteristics.For each individual user, we estimate the predictive accuracy of three recommendation algorithms.We then apply regression-based models to uncover the relationships between rating characteristics and recommendation performance at the individual user level.Our experimental results show consistent and significant effects of several rating measures on recommendation accuracy.Understanding how rating characteristics affect the recommendation performance at the individual user level has practical implications for the design of recommender systems.
Into the Dark: A Case Study of Banned Darknet Drug Forums

In March 2018, to the surprise of many users, the largest Reddit forums related to darknet markets (DNM) were banned overnight. For users, whose trading activity relied heavily on these forums, the ban was a threat to the community as a whole. In this study we use a complete set of posts from the newly founded forums in the darknet-based “Dread” platform to examine key discussion topics and the sentiment of the community towards the ban. We look at the level of user engagement in the new forums, and the number of users who retain their old usernames. Applying topic modelling to posts on the new forum, we show that there are many overlapping themes across both the banned and new forums, and that discussions on drugs are the most prominent, followed by vendors, shipping reviews, and payment methods. We observe that the new community demonstrates negative sentiment toward the unexpected ban and the loss of accumulated information, but also holds a favourable view of Dread in the hopes that it will offer greater features and security for the users. Users across both platforms express attachment and affinity to the general DNM community, and demonstrate relation to it beyond the commercial purpose.
Image Alpha Matting via Residual Convolutional Grid Network

Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and image synthesis. Conventional approaches for alpha matting do not perform well when they encounter complicated background or when foreground and background color distributions overlap. It is also difficult to extract alpha matte accurately when the foreground objects are semi-transparent or hairy. In this paper, we propose a residual convolutional grid network for alpha matting, which deals with those matting problems well and has a performance comparable to the best image matting method in the literature. Meanwhile, the number of parameters in our method is less than one-third of the number of parameters in the best method.
How Much Do Print Backfiles Matter?

As physical collections are increasingly pressed for space, libraries continue to look at practices such as weeding and off-site storage, coupled with services like on-demand article-level document delivery, as potential space solutions. As libraries discard long runs of print journals, though, what role do journal backfiles play going forward? When many see backfiles as space hogs, is there data that provides a compelling argument for keeping backfiles of journals, and, if so, how should those backfiles be handled and by whom? Incorporating information from an interview with Glenn Jaeger, owner of Absolute Backorder Service, interlibrary loan data from the University of Prince Edward Island, and examples from the literature, this Balance Point column looks at the role that print journal backfiles play in the library landscape.
Reconstruction of the 2014-2015 FOGO Volcano (Cape Verde) Eruption Through Thermal Remotely Sensed Imagery

A volcanic eruption initiated on 23/Nov/2014 on Fogo Island in Cape Verde (14.91°N, 24.38°W, Figure 1 ) lasted for 78 days until 08/Feb/2015 [1] - [4] . Volcanic eruptions are phenomena with strong heat fluxes due to the release of lava and gases that can be suitably monitored through thermal remote sensing and its principles [5] . The sensors acquiring such type of data have generally a high temporal resolution but a low spatial one. But at the same time, non-thermal imagery captured by other sensors can also be very useful to better understand the ongoing event and track its temporal evolution [6] . Therefore, we propose a methodology to reconstruct the 2014-2015 Fogo eruption based on multi-platform imagery.
Intermediality of Screens in Post-Media Assemblages

Abstract In contemporary artworks of so-called post-media assemblage, screens can be argued to emphasize, interconnect and rearticulate relationships between various parts in various modalities of image-making and display. They can be understood to produce gesturality that maintains conditions of mediality, which is the sustenance of relations between different parts of the media ensemble. This paper is an attempt to understand screens by analysing the gesturality that they propagate and not just facilitate. For this purpose, the paper interrogates the intermediality of screens in contemporary media arts that rely on this gesturality. By closely analysing contemporary media art installations such as Solar Reserve (Tonopah, Nevada) (John Gerrard, 2014) and Shadow 3 (Shilpa Gupta, 2007), this paper elaborates a concept of intermediality as an unfixed state and describes in-betweenness as enabling an openness to continuously form, unform and deform relations with different entities, thereby producing a gestural modality.
Porosity distribution prediction of untapped Gumai Formation by applying multi-attribute analysis: A case study in South Sumatra Basin

SummaryGumai Formation is often regarded as merely regional seal in South Sumatra Basin as it is dominated by shale, claystone and siltstone. However, a variety of geological and geophysical approach has been conducted in Northeast Betara (NEB) field to boost the confidence of hydrocarbon bearing reservoir distribution in the untapped formation. A proprietary seismic attribute screening approach is developed for this study in order to optimise the match between modelled and actual porosity logs. Multi-attribute analysis, neural properties, density, gamma rays, and porosity are utilized to discriminate sand, thin sand and shale. Neural network analysis is done to improve the pseudo volume results including pseudo gamma-ray and pseudo porosity which are then used for geological interpretation. Validation is done by examining existing mud log data on several wells. Good result from multi-attribute and neural network analysis is exploited to map the distribution of lithology and porosity. The result is then overlaid on depth structure map and shows a good match between predicted porosity and actual porosity of the well. Training results and validation values also shows good correlation and validation. Correlation value of 0.6005 and 0.8060 are achieved by multi-attribute linear regression and Probabilistic Neural Network (PNN), respectively. Northwestern part of the field is considered having promising reservoir distribution and porosity. Average density of 2.0 to 2.25 g/cc and average porosity of 14 to 16% are inferred to be present in the reservoir based on this study.
Implementation of moving target detection and tracking algorithms based on FPGA

In order to improve the real-time and portability of the moving target detection and tracking (MTDT) system, a compact FPGA-based MTDT system is built in this paper by using the parallel computing and flexible programming of Field Programmable Gate Array (FPGA). In order to realize the detection and tracking of moving targets on resource -constrained FPGA, some MTDT algorithms is analyzed firstly, and appropriate modifications were made without changing the basic principles to make it adapt to the limited logical resources of the Xilinx Spartan -6 series of FPGA chip selected in this design. Then the FPGA system is composed of four units: the image acquisition unit, the image storage unit, the image pre-and post- processing unit and the image display unit. Two image difference methods, an inter-frame and a background difference method, are implemented in the system. Finally, the moving target can be directly indicated on a video graphics array (VGA) displayer in the image display unit. The test results show that the system can detect and track a single target in real time in various resolutions at various frame rates, i.e. VGA @30fps and 15fps, 720p @15fps.