Reviewerly/exHarmony huggingface dataset:
Cybercrime as a Consequence of Unreasonable Expectations

The studies published by the Global Drugs Survey research team<sup>1,2</sup> indicate that using the dark web to acquire illicit drugs is increasingly common. It is perceived as a safer alternative to the more traditional routes of obtaining drugs, such as lysergic acid diethylamide, which are difficult to detect in the postal mail without opening the packaging.
An efficient algorithm for computing the edit distance with non-overlapping inversions

Given two sequences with lengths m and n, the edit distance is the minimum cost required to transform one into the other. The classical operations involved in the traditional edit distance problem include insertion, deletion and replacement. In 2004, Gao et al. [5] proposed an algorithm for calculating the edit distance with non-overlapping inversion, where an inversion may overlap the classical operations, but not another inversion. The time complexity of their algorithm is O(m2n2). In this paper, we introduce a new variant of the traditional edit distance problem, the edit distance with non-overlapping inversion (EDI) problem, which is to determine the minimum edit distance with four operations: insertion, deletion, replacement and inversion. The used operations cannot overlap one another. We propose an efficient algorithm for the EDI problem. The time complexity is O(m2n) in the worst case and O(mn) in the average case.
Image Indexing Techniques

Images are described by various forms of feature descriptors. Especially local invariant features have gained a wide popularity Lowe (Int J Comput Vis 60:91–110, 2004 [17]), Matas et al. (Image Vis Comput 22:761–767, 2004 [18]), Mikolajczyk et al. (Int J Comput Vis 60:63–86, 2004 [20]), Nister Stewenius (Scalable recognition with a vocabulary tree, pp. 2161–2168, 2006 [25]) and Sivic and Zisserman (Video google: a text retrieval approach to object matching in videos, pp. 1470–1477, 2003 [35]).
GA-guided task planning for multiple-HAPS in realistic time-varying operation environments

High-Altitude Pseudo-Satellites (HAPS) are long-endurance, fixed-wing, lightweight Unmanned Aerial Vehicles (UAVs) that operate in the stratosphere and offer a flexible alternative for ground activity monitoring/imaging at specific time windows. As their missions must be planned ahead (to let them operate in controlled airspace), this paper presents a Genetic Algorithm (GA)-guided Hierarchical Task Network (HTN)-based planner for multiple HAPS. The HTN allows to compute plans that conform with airspace regulations and operation protocols. The GA copes with the exponentially growing complexity (with the number of monitoring locations and involved HAPS) of the combinatorial problem to search for an optimal task decomposition (that considers the time-dependent mission requirements and the time-varying environment). Besides, the GA offers a flexible way to handle the problem constraints and optimization criteria: the former encodes the airspace regulations, while the latter measures the client satisfaction, the operation efficiency and the normalized expected mission reward (that considers the wind effects in the uncertainty of the arrival-times at the monitoring-locations). Finally, by integrating the GA into the HTN planner, the new approach efficiently finds overall good task decompositions, leading to satisfactory task plans that can be executed reliably (even in tough environments), as the results in the paper show.
SYNERGISTIC APPROACH TO THE TEACHING OF INFORMATICS AS AN ELEMENT OF THE DEVELOPMENT OF KEY COMPETENCIES OF STUDENTS

The article discusses the possibility of implementing modern educational tasks using a synergistic approach to the teaching of informatics. Lessons are presented within the framework of a research project using synergy ideas.
Enhanced Automatic Image Parameter setting and Segmentation Method

There are a lot of works done to automatically set parameters and segment images based on Pulse Coupled Neural Networks (PCNN). In this study we propose an automatic parameters setting and segmentation method based on Intersecting Cortical Mode (ICM) which enables to overcome the basic limitation of PCNN based methods. We used the ICM as base and developed an enhanced automatic method which can withstand effects of multiple background and illumination during segmentation. Characteristics pixel values of the input image are used to deduce corresponding segmentation parameters. The experiment is done on Aerial Image Segmentation Dataset and Database of Human Segmented Natural Images. Our method outperformed for subjective and objective evaluations, also shown consistent assignment of parameter values. Also the proposed method is able to reduce the segmentation time by half and overcome the limitations of the existing automatic models.
Complex Method of Video Stream Intensity Control

Consider the construction of a video stream bit rate controlling method, based on the non-equilibrium positional coding of a transformants bit representation. Developed the concept of combined use of technologies, which allows to adapt the video data intensity to the channel bandwidth in the conditions of its dynamic change.
Quality Evaluation of Steganographic Embedding in Video Traffic

In Volgograd State University research on safe and reliable data transmission in the video stream as a container is carried out. Video data is currently the most common data transmitted over the network. The existing methods of steganography are analyzed. The best method is chosen. It is used to construct the protocol of data transmission. The protocol includes the encryption of transmitted data and calculation of their digital signature. After that, the data is embedded in the video stream with the selected method and transmitted over an open communication channel. On the receiving side, the data is extracted from the video stream and the digital signature is verified. After that, the data is decrypted and transmitted to the recipient. To assess the effectiveness of the proposed method of embedding data in a video stream for subsequent transmission, it is necessary to use methods of steganalysis. Based on one of the approaches to the detection of data embedded in the container, a prototype conducting steganalysis was developed. The efficiency of the data detection in the steganocontainers with different characteristics is assessed.
Camera Source Identification

Camera source identification is one of the fundamental problems in digital image forensics. In this chapter, at the outset, the reader is made familiar with the basic components of a modern digital camera, along with the processing, acquisition, and storage of digital images in camera sources. Then a detailed literature review has been done for efficient, blind camera source identification. One of the major and effective solutions to the problem is to extract appropriate features from the images, then train a classifier, and finally classify the test samples using that trained classifier. In this chapter, we have discussed a camera source identification methodology, based on extraction of the discrete cosine transform residual features, and subsequent random forest-based ensemble classification with AdaBoost. The classification accuracy was further improved by incorporating dimensionality reduction by principal component analysis. The experiments were performed on the Dresden Image Database, and the state-of-the-art techniques were compared in detail. Moreover, the proposed technique shows low overfitting trends when the constructed classifier for one image database is applied to a different image database.
The robots won't steal your jobs, it will be worse than that

Automated bots are fragmenting jobs in a way that undermines our ability to find full-time work, warns Annalee Newitz
Improved Weighted Least Square Radiometric Calibration Based Noise and Outlier Rejection by Adjacent Comparagraph and Brightness Transfer Function

In this paper, we propose an improved radiometric calibration algorithm by extending the previous radiometric calibration method that is based on the “fixed exposure ratio” Brightness Transfer Function (BTF) and Weight Least Square. Our proposed method can be applied to the radiometric calibration on the case that the images are captured with non-constant exposure ratios which makes the image acquisition process to be more flexible. The key idea of our new proposed method is to determine a set of BTFs between the set of consecutive image pairs and then to form the set of composited functions. From the composition, we then convert the BTFs of all image pairs into the virtually single BTF. The noise & outlier rejection based on this BTF is applied to retrieve the clean data. Finally, a reformulation of weighted least square minimization is proposed to estimate the camera response function (CRF) of a camera. From the performance of our proposed algorithm in comparison with the state-of-the-art least square method proposed by Mitsunaga and Nayar as the baseline, we found that our method outperforms the baseline algorithm on both the synthetic dataset and the dataset of real-world images.
Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation

The big data trend has inspired feature-driven learning tasks, which cannot be handled by conventional machine learning models. Unstructured data produces very large binary matrices with millions of columns when converted to vector form. However, such data is often sparse, and hence can be manageable through the use of sparse random projections. This work studies efficient non-iterative and iterative methods suitable for such data, evaluating the results on two representative machine learning tasks with millions of samples and features. An efficient Jaccard kernel is introduced as an alternative to the sparse random projection. Findings indicate that non-iterative methods can find larger, more accurate models than iterative methods in different application scenarios.
Composite FPGA-based Accelerator for Deep Convolutional Neural Networks

Convolutional neural network (CNN) can achieve high prediction accuracy while they built complex models; however, high power consumption, memory demands, and bandwidth resource consumption by the models are creating enormous challenges in their actual deployment. To achieve the rapid prediction capabilities of high-precision models, a modified convolutional neural network model based on the GoogLeNet model has been proposed in this work, and a composite-structure convolutional neural network hardware accelerator compatible with the parallel computing model has been designed. An experimental model based the modified structure has been established and CIFAR-10 dataset was used to evaluate the prediction accuracy. The accelerator achieved 663 FPS peak performance with a 9.06% error rate, and was implemented on a Xilinx VC709. Compared to CPU and GPU, its energy efficiency increased by a factor of 7.1 and 1.9, respectively, achieving a high-precision complex network computing acceleration.
[Paper] Statistical Evaluation of Color Components for Pupil Detection and Diameter Measurement Under Visible-light Conditions

A variety of systems have been developed that use information on pupil diameter variation. These systems typically require a general-purpose PC or a portable terminal. In addition, a near-infrared camera and an illumination system are required to obtain pupil diameter information. However, a near-infrared camera and an appropriate illumination source are usually not installed in conventional PCs or mobile devices. If the pupil diameter can be measured using a visible-light camera equipped with a general-purpose PC or portable terminal, the aforementioned systems could become more widely used. However, it is difficult to discriminate between the iris and pupil regions in an image captured using a visible-light camera because the colors of the iris and the pupil are similar in individuals of Asian ancestry. In this study, we determine effective color components for the detection and diameter measurement of the pupil under visible light conditions based on eye images obtained using a visible-light camera. The experimental results revealed that the effective color components for the detection and diameter measurement of the pupil are the R, Y, and K color components for brown eyes and the R and C color components for hazel eyes. The suitable color components differ depending on the color of the iris.
Mobile Internet Application Classification Based on Machine Learning

Service classification technology helps service suppliers understand how to use network services that offer personalized services to users. In this study, machine learning technology is used to classify such services, called mobile Internet applications (APPs). Firstly traffic of different application is collected and is used to build model by using C4.5 and SVM algorithm. Secondly, the model is used to classify the application type from the mobile internet traffic. Then traffic of applications is merged as major class by application type such as web browsing, e-commerce and the above two algorithms are applied to classify the major class that different applications belong to. Finally the Precision and Recall ration of two algorithms are compared and analyzed. Mobile stream video/audio is better recognized than Mobile app using http by using machine learning method. In order to improve the mobile application classification result, the only machine learning algorithm is not enough.
Unsupervised Deep Learning in Character Recognition

The recognition of handwritten digits is a well-researched problem and has many applications in real life. The important applications include automatic reading of addresses on postal envelopes, automated form processing, automated processing of handwritten bank cheques, and filled-in forms like questionnaires or money orders. Digit recognition serves as an evaluation task because the problem is well defined and benchmark datasets are easily available.
Agreements on Administration of Titles to Knowhow

Introduction.Knowhow is confidential information in the field of intellectual property, which, in particular, implies the specific features of administering intellectual property rights for this object.The theory assumes that there is one legal mechanism to administer intellectual property rights to knowhow that is knowhow transfer agreement, but, at the same time, the transfer of the title to knowhow is understood as granting the right to use it.As a result, practically it is difficult to understand what agreement is to be used to alienate the right to knowhow and to grant the right to use it. Problem Statement. The applicable national intellectual property legislation has not been harmonized. The terms and definitions used in the general and the special legislation for contractual mechanisms of administering intellectual property rights are different, which causes their dual understanding and misunderstanding of the main purpose of agreements on administration of intellectual property rights, which is the administration of the very property rights of intellectual property, not by the objects of intellectual property rights. Accordingly, the choice of contractual mechanism for administering the rights to knowhow is problematic, taking into account the specific nature of the subject of the research associated with its confidentiality.Purpose.To identify possible contractual mechanisms for administering property rights to knowhow. Materials and methods.The methodological framework of the research is based on the following methods of scholarly knowledge: the general philosophical (in particular, dialectical), the general scholarly (namely, formal logical, structural, comparative and others), and on the special methods of scholarly knowledge used in legal science (for example, comparative, formal and legal, special, etc.). Results. A comparative legal analysis of civil law contracts, including agreements on the administration of intellec tual property rights in terms of the possibility of their use in order to grant for use and to alienate title to knowhow has been done.Conclusions.The administration of intellectual property rights to knowhow can be done using all existing contractual mechanisms specified for objects of intellectual property rights in the Civil Code of Ukraine.However, since the freedom of agreement is one of the fundamental principles of the civil law, the separate introduction of knowhow transfer agreement may exist, but in the subject of this agreement it is necessary to clearly identify which exactly rights are granted under this agreement either the right of use or the right of alienation.When concluding agreements on the administration of property rights to knowhow, it is necessary to take into consideration the confidential nature of knowhow.
International Standards of Education and Their Evolution

The article is dedicated to the problems of international educational standards. The areas in which at present at the national and international levels active work is underway on standardization and drawing up a unifi ed system of requirements are identifi ed. The defi nition of educational activity, which is understood as a purposeful activity involving a certain form of communication aimed at learning, is clarifi ed. The stages of development of standards of the Bologna process are allocated. In accordance with the main objectives and requirements of the Bologna process, the main standards and guidelines for assessing and ensuring the quality of higher education developed and published by ENQA are grouped into three groups: internal standards for ensuring the quality of education; standards for assessing the quality of the educational process by the external environment; standards for the activities of specialized agencies for assessing the quality of education. It covers major aspects of internal and external quality standards.
Extending $$\mathscr {E\!L}^{++}$$ with Linear Constraints on the Probability of Axioms

One of the main reasons to employ a description logic such as $$\mathscr {E\!L}^{++}$$ is the fact that it has efficient, polynomial-time algorithmic properties such as deciding consistency and inferring subsumption. However, simply by adding negation of concepts to it, we obtain the expressivity of description logics whose decision procedure is ExpTime-complete. Similar complexity explosion occurs if we add probability assignments on concepts. To lower the resulting complexity, we instead concentrate on assigning probabilities to Axioms/GCIs. We show that the consistency detection problem for such a probabilistic description logic is NP-complete, and present a linear algebraic deterministic algorithm to solve it, using the column generation technique. We also examine and provide algorithms for the probabilistic extension problem, which consists of inferring the minimum and maximum probabilities for a new axiom, given a consistent probabilistic knowledge base.
FORMATION OF METASUBJECT COMPETENCIES IN THE COURSE “INFORMATION TECHNOLOGIES” BY MEANS OF THE BIG DATA PROCESSING LANGUAGE R

The article describes the training methods in the course “Information Technologies” for the future bachelors of the directions “Economics”, “Management”, “Finance”, “Business Informatics”, the development of metasubject competencies of the student while his use of tools for data processing by means of the language R. The metasubject essence of the work is to update traditional economic knowledge and skills through various presentation forms of the same data sets. As part of the laboratory work described in the article, future bachelors learn to use the basic tools of the R language and acquire specific skills and abilities in R-Studio using the example of processing currency exchange data. The description of the methods is presented in the form of the traditional Key-by-Key technology, which is widely used in teaching information technologies.
Fast Combination Method for Dependent Evidences in the Framework of Hyper-Power Sets

Two dependent evidences can be viewed as resulted from orthogonal sum of one dependent original evidence and two independent original evidences, respectively. The original method has so many iterations and big calculation, based on these disadvantages, a fast combination method for dependent evidences in the framework of hyper-power sets is proposed in this paper. Equipollent classic Dezert-Smarandache (DSm) rule of combination can be got through importing the commonality function, according to the results of model analysis, theorem proving and example comparison show the feasibility and effectiveness of the proposed method.
Research on demand-driven service mechanism in SDEN

On-demand customization and dynamic expansion of network services are the trend and core of future network development, customized network services based on user demands will become one of the core functions of the future network. Focuses on the key issue in SDEN(Service Dynamic Extended Network) network -- demand-driven services, this paper firstly summarizes the SDEN network principle, then proposes a demand description method based on ontology, and the mapping of user requirements to service instances is realized, so as to realize the demand-driven service mechanism. Finally, the validity and feasibility of the method are verified by a detailed example.
Sparse Manifold Embedded Hashing for Multimedia Retrieval

Hashing has become more and more attractive in the large-scale multimedia retrieval community, due to its fast search speed and low storage cost. Most hashing methods focus on finding the inherent data structure, and neglect the sparse reconstruction relationship. Besides, most of them adopt a two-step solution for the structure embedding and the hash codes learning, which may yield suboptimal results. To address these issues, in this paper, we present a novel sparsity-based hashing method, namely, Sparse Manifold embedded hASHing, SMASH for short. It employs the sparse representation technique to extract the implicit structure in the data, and preserves the structure by minimizing the reconstruction error and the quantization loss with constraints to satisfy the independence and balance of the hash codes. An alternative algorithm is devised to solve the optimization problem in SMASH. Based on it, SMASH learns the hash codes and the hash functions simultaneously. Extensive experiments on several benchmark datasets demonstrate that SMASH outperforms some state-of-the-art hashing methods for the multimedia retrieval task.
Value-of-Information Driven Content Presentation and Filtering in Military Geographic Information Systems

Military C3I (Command, Control, Communications and Intelligence) operations at the tactical level have come to rely upon Geographic Information Systems (GIS) to access geotagged information relevant to mission needs. Management of Information Objects within military GIS applications presents a number of known research challenges, tied both to selection of mission-appropriate information and management of Soldier attention. Towards supporting prioritized content delivery and filtering within tactical networks, methods based on the estimated Value of Information (VoI) specific to mission context have demonstrated prior performance benefits (e.g., conservation of network bandwidth). However, limited HCI-themed research has been conducted to-date on Soldier interaction with VoI-augmented GIS applications, including assessment of their impact on situational awareness over areas of operation. This paper presents foundational work for studying Soldier interaction with VoI-augmented GIS applications, covering: (1) a supporting experimental platform based on the Android Tactical Assault Kit (ATAK); (2) a pilot study conducted with Soldiers, to assess the impact of VoI-based content prioritization on user experience and Information Object review.