Reviewerly/exHarmony huggingface dataset:
A Generalized Kernel Risk Sensitive Loss for Robust Two-Dimensional Singular Value Decomposition

Two-dimensional singular value decomposition (2DSVD) is an important dimensionality reduction algorithm which has inherent advantage in preserving the structure of 2D images. However, 2DSVD algorithm is based on the squared error loss, which may exaggerate the projection errors with the presence of outliers. To solve this problem, we propose a generalized kernel risk sensitive loss for measuring the projection error in 2DSVD, which automatically eliminates the outlier information during optimization. Since the proposed objective function is non-convex, a majorization-minimization algorithm is developed to efficiently solve it. Our method is rotational invariant and has intrinsic advantages in processing non-centered data. Experimental results on public databases demonstrate that the performance of the proposed method significantly outperforms several benchmark methods on different applications.
A General Systems Approach to Cloud Computing Security Issues

An intensive stream of messages about the problem of cloud computing security and a significant number of proposals to mitigate and prevent violation of data privacy and the integrity of the cloud computing environment indicate the relevance and significance of the problem.To bring everything into a certain system is the task of this chapter.We use different methodological approaches in order to find such an integrated solution to the combination of these approaches that, on a unified methodological basis, would allow us to look at the whole range of widening issues of ensuring security and the organization of thinking and activity in the near future.This approach allows us to identify additional problems in this area and outline a program for their development.We try to build a system of methodological design and research over the many private methodologies that authors of articles usually use, relying on the experience of generalizing and concretizing system approaches, and, in particular, expanding geographical and historical boundaries, including system generalizations of intercultural studies and philosophical movements.An attempt is made to disassemble the security problem of cloud computing into a certain number of layers, processes, and technologies of thinking, and to reconnect them into a single whole with the character of thinking and activity.
High-Security Image Watermarking with Multilevel Embedding Using Various Image Transform Techniques

Digital image watermarking techniques are used to provide authentication for secured data transmission. In this paper, multilevel embedding approach is addressed for high security. Initially the cover image is converted into YUV model. Now apply Discrete Wavelet Transform (DWT) on Y component of cover image and watermark images. In further steps, Discrete Fourier Transform (DFT) and Singular Value Decomposition (SVD) are applied sequentially on LH component of Y and watermark images for high security. Finally embedding process is done on these details using scaling factor. At extraction stage, inverse of embedding process is carried to retrieve the cover image. The proposed method is good based on qualitative and quantitative analysis than the existing methods like DWT, DWT-SVD, etc.
Correlation Structured Low-Rank Subspace Clustering

High-dimensional data is usually drawn from the union of multiple low-dimensional subspaces. The task of subspace clustering is to cluster high-dimensional data into their corresponding subspace. Subspace clustering algorithm based on spectral clustering is one of the important methods of subspace clustering. This method is proposed by the spectral clustering framework. This type of method is usually divided into two steps, namely learning the affinity matrix and obtaining the segmentation matrix by spectral clustering. However, the results obtained this way are often sub-optimal. Meanwhile many state-of-the-art algorithms do not consider consistency of correlation data, resulting in the affinity matrix either too dense to make the algorithm contain a lot of erroneous information, or too sparse to make the algorithm lose correlation information. In this paper, we propose Correlation Structured Low-Rank Subspace Clustering (CSLRSC). The CSLRSC is based on the model of low rank representation by adding new <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{1}$</tex> norm to solve the problem that the affinity matrix established by the previous method is too dense. Adding the structured norm to the model can integrate affinity matrix learning and spectral clustering into one step to obtain an approximate optimal solution of the algorithm. The experimental results that we obtain on Extended Yale B face dataset, MINIST hand-write dataset, and ORL dataset show that our algorithm is superior to others.
Partial Discharge Signal Denoising Based on DualTree Complex Wavelet Transform Combined with Thresholding

In high voltage engineering, it is of great importance to monitor partial discharge (PD) of critical apparatus. On-site PD signal is usually contaminated by white noise. Therefore, it is necessary to denoise the measured signal to acquire real PD. The most widely used method is the traditional wavelet thresholding. In this paper, a modified version for the traditional wavelet thresholding is proposed, i.e. dual-tree complex wavelet transform (DTCWT) is used to replace single wavelet to be combined with thresholding. Two DTCWT examples are used, i.e. dual tree wavelet pair constructed by common factor method, and dual tree wavelet pair constructed by Db family wavelets. To verify their performance, a numeric study is carried out, and the computation results show that DTCWT combined with thresholding is better than traditional wavelet thresholding.
Supervised Term Weight Training for Improving Question-Knowledge Matching in Chatbots

We design several supervised methods of training term weights for improving results of question-knowledge matching task on a chatbot system-AliMe. Considering demand of high QPS (Query Per Second), we take a traditional regression model as the matching model rather than deep models.
pyTAG: python-based interactive training data generation for visual tracking algorithms (Conference Presentation)

In this study, a rapid training data and ground truth generation tool has been implemented for visual tracking. The proposed tool's plugin structure allows integration, testing, and validation of different trackers. The tracker can be paused, resumed, forwarded, rewound and re-initialized on the run, after it loses the object, which is a needed step in the training data generation. This tool has been implemented to assist researchers to rapidly generate ground truth and training data, fix annotations, run and visualize their own single object trackers, or existing object tracking techniques.
The Minimum Cost of Information Erasure for Stationary Memoryless Sources under Restriction on the Output Distribution

In order to erase data including confidential in-formation stored in storage devices, an unrelated and random sequence is usually overwritten, which prevents the data from being restored. The problem of minimizing the cost for information erasure when the amount of information leakage of the confidential information should be less than or equal to a constant asymptotically has been introduced by T. Matsuta and T. Uyematsu. Whereas the minimum cost for overwriting has been given for general sources, a single-letter characterization for stationary memoryless sources is not easily derived. In this paper, we give single-letter characterizations for stationary memoryless sources under two types of restrictions: one requires the output distribution of the encoder to be independent and identically distributed (i.i.d.) and the other requires it to be memoryless but not necessarily i.i.d. asymptotically. The characterizations indicate the relation among the amount of information leakage, the minimum cost for information erasure and the rate of the size of uniformly distributed sequences. The obtained results show that the minimum costs are different between these restrictions.
Investigating Elliptic Curve Cryptography for Securing Smart Grid Environments

This paper evaluates the performance of an elliptic curve based approach for the task of data aggregation in smart grids. This scheme supports a distributed data aggregation approach, where smart meters at each level of the tree aggregate results from their children and transmit it to their parents. We use homomorphic encryption to ensure the privacy of encrypted power usage data during its transmission among the smart meters. Power usage statistics are aggregated at each level of the network tree, and a smart meter shall not gain any information on the encrypted data received by it. We discuss some advantages of using elliptic curves to ensure additive homomorphism, and justify some of its shortcomings and performance issues. We also consider possible improvements that allows the proposed system to perform well in the environment of smart grid system. Finally, we compare the proposed scheme to the Paillier cryptosystem using the Mosaik smart grid simulation framework.
Sarcasm Detection Using Multiple Occurrences of Emojis and Hashtagged Words

The emojis have become an essential part of communication in text, mostly in an informal platform like social media where millions of people share content online. Another important trend is hashtags where people share the content on a particular event, and the hashtags are also used to create a small group of posts having views on similar topics. This paper proposes the use of both these recent additions in communication as cues to identify the presence of sarcasm in the post. The presence of multiple emojis is considered along with the text and hashtagged words to compare the polarities which are further processed to find the sarcasm. In this paper, naïve Bayes approach is used where polarities of multiple emojis, text, and hashtagged words are calculated and compared to reach the final solution. The work on using multiple emojis present in a post to find the sarcasm is the first of its kind.
Typed Object Theory

The main features of typed object theory are: (1) it is formalized in a relational type theory in which relations are primitive and are not reconstructed as functions, (2) it contains a formal theory of relations, with identity conditions that allow for the hyperintensionality of relations, (3) the domain of each type t divides up into ordinary entities of type t and abstract entities of type t, and (4) the denotation and the sense of a natural language term of type t are assigned objects of the very same type, though the sense of a term of type t is always an abstract object of that type. We show how typed object theory compares with other intensional type theories. For example, typed object theory doesn’t require a primitive type for truth-values, doesn’t require a primitive type for possible worlds, doesn’t collapse the types for individuals and propositions (as suggested by Partee, Liefke, and Liefke and Werning); and uses only one set of types for both its syntax and semantics (unlike Williamson’s logic). Finally, we analyze a number of natural language contexts without requiring the technique of ‘type-raising’.
READABILITY OF THE SECOND GRADE ARABIC LANGUAGE BOOKS IN THE UNITED ARAB EMIRATES

Purpose of the study: This study aims to measure the readability of Second Grade Arabic Language books in the United Arab Emirates (UAE).&#x0D; Methodology: This paper discusses a quantitative survey conducted, based on previous studies which include Momni and Momni (2011), Nasser and Ibrahimi (2013) and Lami and Zoaeni (2014), to examine readability problems in Arabic language books used in UAE schools. The researchers developed a Cloze Test and performed the test on a randomly selected sample of 387 Second Grade students. Both descriptive and inferential statistics (T-Test, ANOVA) were analysed using SPSS version 22 and to examine the readability of Second Grade Arabic Language books.&#x0D; Main Findings: The result indicates that the students’ average score in all the readability levels to the total score of the Cloze Test is at an unsatisfactory level. There are significant differences in the level of the poetic and prose texts’ readability among the genders and different academic averages.&#x0D; Applications of this study: This study provides evidence that the long-term goal of the Abu Dhabi Educational Council (ADEC) has yet to be achieved. The outcomes suggest that there is a need to improve students' readability of Arabic Language books in the UAE, starting from the Second Grade.&#x0D; Novelty/Originality of this study: Readability of Arabic Language Book was widely explained by other researchers, but there is no study conducted on Second Grade students in UAE.
Facial Identification using Haar Cascading with BRISK

A swift and efficient facial identification system is a well-known discipline of computer vision applications and thus forms a pivotal part of image processing. Despite the advantages of existing methods, there is still a great demand for modifications in these algorithms in order to close to lacks of proposed methods. In this proposed work, the image is preprocessed with Contrast-limited Adaptive Histogram Equalization and faces are detected with Haar-Cascading (Viola-Jones algorithm). Then face identification is done using BRISK (Binary Robust Invariant Scalable Key-points) descriptor. Experimental results demonstrate that the proposed methodology achieves better facial identification even under various challenging conditions compared with the existing BRISK.
Incremental Sequential Rule Mining with Streaming Input Traces

Traditional static pattern mining techniques, such as association rule mining and sequential pattern mining, perform inefficiently when applied to streaming data when regular updates are required, since there is significant repetition in the computation. Incremental mining techniques instead reuse information that has been previously extracted, and apply newly received data to compute the updated set of patterns. This paper proposes a new algorithm for incrementally mining sequential rules with streaming data. An existing rule mining algorithm, ERMiner is presented, and an incremental extension, called IERMiner is proposed and demonstrated. Experiments show that IERMiner significantly decreases the run time required to update the set of patterns when compared to running ERMiner on the full dataset each time.
EDUCATION OF TOLERANCE IN SCHOOLCHILDREN IN THE CONTEXT OF A POLYCULTURAL SOCIETY

Polycultural education is considered as a process of creating conditions forschoolchildren to develop a worldview setting for constructive cooperation by engaging in ethnic, native and world cultures, developing planetary consciousness on this basis, forming readiness and ability to live in a multinational society. The process of education of tolerance in schoolchildren a multicultural educational environment is considered in three aspects: acquiring a particular philosophy of tolerance as a value; culture of tolerant consciousness; attitude to the appropriate reality that is embodied in real behaviour. The conditions for the efficiency of education of tolerance in schoolchildren in the context of a multicultural society are: multicultural content of the educational process; organization of a tolerant environment that ensures tolerant interaction between the subjects of the educational process; the use of interactive, personalityoriented, reflexive and dialogic technologies, which are aimed to develop the tolerant qualities of schoolchildren; developed tolerant qualities of the teacher’s personality and his/her competence in the issue of education of tolerance.
Genome Assembly Using Reinforcement Learning

Reinforcement learning (RL) aims to build intelligent agents able to optimally act after the training process to solve a given goal task in an autonomous and non-deterministic fashion. It has been successfully employed in several areas; however, few RL-based approaches related to genome assembly have been found, especially when considering real input datasets. De novo genome assembly is a crucial step in a number of genome projects, but due to its high complexity, the outcome of state-of-art assemblers is still insufficient to assist researchers in answering all their scientific questions properly. Hence, the development of better assembler is desirable and perhaps necessary, and preliminary studies suggest that RL has the potential to solve this computational task. In this sense, this paper presents an empirical analysis to evaluate this hypothesis, particularly in higher scale, through performance assessment along with time and space complexity analysis of a theoretical approach to the problem of assembly proposed by [2] using the RL algorithm Q-learning. Our analysis shows that, although space and time complexities are limiting scale issues, RL is shown as a viable alternative for solving the DNA fragment assembly problem.
Effective method for Shape based Image Retrieval using Pyramid of Histogram of Oriented Gradients

Shape Based Image Retrieval (SBIR) is a technique to retrieve images from the database by extracting the shape features of the object. In SBIR, shape features are extracted and then similarity matching is carried out based on some measure. Pyramid of Histogram of Oriented Gradients (PHOG), an improvised version of Histogram of Oriented Gradients (HOG), is very efficient for calculation of feature vector of an image. In this paper, PHOG along with Locality Sensitive Hashing has been exploited as a novel approach to SBIR. Locality Sensitive Hashing (LSH) is used for bit-level similarity matching.
A Cross-cultural Aspect of the Technical Writer Profession

The main objective of this article is to define the work of a technical writer as a cross-cultural professional. At present, any new device, gadget, appliance, software, etc. is to come with a user manual or instruction aimed at establishing communication between a manufacturer and an end-user. If you are a lay reader, it is often difficult to fully realize the functions of complex products within a short term. Thus, a developed technical documentation set is not only to train a user to work with a definite product quickly and effectively, but also to become an agent between the user and the manufacturer. Establishing and maintaining such communication is one of the most important tasks of a technical writer as a professional participating in cross-cultural connection while designing a set of technical documentation. These aspects of the technical writer’s working functions, as well as the main methods used when developing user manuals and instructions are viewed in the present paper. Special attention is paid to the idea of providing different groups of people with relevant and updated technical information.
Identifying Expert Intent Queries

Search engines assess the quality of their results using [email protected] for the queries in regular judgement sets. Since these sets are human annotated, the judgement sets are limited in size. Hence, these sets may not have proper representation from “Expert Topics”, e.g., <Machine Learning>, if an Expert Topic represents a tail segment. Therefore, query sets specific to expert topics are prepared to assess the search quality on such topics, which are judged by the domain experts. Such expert judgements are typically 5X-10X more expensive compared to regular judgements. Hence, “Expert Query Sets” must be prepared such that these queries are likely to be judged differently by an expert and a regular judge.
Themes and Difficulties in Distributed Agile Email Activity

In a previous study by Niinimaki, the main use of emails in a distributed setting was found to be for sending non-urgent, group-wide information. In this paper, we delve deeper into this question and examine interview text from seven industrial development staff in a distributed agile setting to explore the underlying rationale for using email. Exploring communication and co-ordination patterns between teams was the chief motivation for the interviews and study. Results showed that while, in some cases, email was indeed used for team-wide communication (in support of the earlier work) a number of other uses and a set of email 'themes' emerged. We examine these themes and compare them with a set of fourteen communication difficulties associated with GSD listed by Monasor and detailed in a Systematic Literature Review. Preliminary findings from our study suggest further that email reflects a microcosm of many of the listed difficulties, but only for half of the total. The overarching conclusion is that, while email might be perceived as relatively unimportant to GSD activity, it is often symptomatic of larger (and recognized) issues and challenges often underpinned by the personalities involved.
Multi‐view city‐based approach for code‐smell evolution visualisation

Code smells are indicators of inappropriate and possibly harmful design decisions that could lead to issues in the comprehensibility and maintainability of software systems. To avoid such quality complications, understanding the presence and prioritising the removal of code smells are required. This study presents a visualisation approach to help better understanding the evolutional characteristics of code smells presented in the different versions of the software system. The core of the visualisation approach is the metaphor of buildings and building blocks. An overall framework for detecting, categorising and visualising code smells is proposed. Three types of code smells were considered in this study. The considered code smells are God Class, Long Method and Type Checking. The applicability of the proposed approach is demonstrated by evaluating several versions of an open-source java software and visualising the detected code smells. Additionally, a pilot experimental study is conducted to empirically assure the usefulness of the proposed visualisations.
Damages for Noneconomic Harm in Intellectual Property Law

This article provides a comprehensive analysis of awards of “non-economic” damages for reputational and emotional harm in intellectual property (IP) law, including trademarks, copyright and moral rights, the right of publicity, and patent law. The article discusses, among other matters, the Second Circuit’s recent decision in Castillo v. G&M Realty LP, affirming a $6.75 million award of statutory damages for the infringement of artists’ moral rights in graffiti art; the European Union’s Intellectual Property Rights Enforcement Directive and its 2016 Liffers decision, which appear to require member states to award, where warranted, non-economic (“moral prejudice”) damages across the full range of IP cases; and some recent arguments in favor of awarding damages for emotional harm in, even, patent infringement actions. Prompted by these and other developments, I argue that courts should recognize reputational harm as a potentially cognizable injury throughout all of the branches of IP law, but that damages for emotional harm should be limited to right of publicity and moral rights matters. In addition, I discuss the various options for providing monetary relief in response to non-economic harm, including awards of general damages, statutory damages, disgorgement of the infringer’s profits, and enhanced or punitive damages; and I conclude with a set of recommendations for crafting awards in a manner that would both vindicate the relevant, cognizable interests of plaintiffs while reducing the risks of arbitrary, uncertain, and potentially over-deterrent relief.