Reviewerly/exHarmony huggingface dataset:
Dynamic Texture Classification with Relative Phase Information in the Complex Wavelet Domain

In recent years, dynamic texture classification has caused widespread concern in the image sequence analysis field. We propose a new method of combining relative phase information of dynamic texture in the complex wavelet domain with probability distribution models for dynamic classification in this paper. Instead of using only real or magnitude information of dynamic texture, relative phase information is an effective complementary measure for dynamic texture classification. Firstly, the finite mixtures of Von Mises distributions (MoVMD) and corresponding parameter estimation method based on expectation-maximization (EM) algorithm are introduced. Subsequently, the dynamic texture features based on MoVMD model for dynamic texture classification are proposed. Besides, the relative phase information of dynamic texture is modeled with MoVMDs after decomposing dynamic texture with the dual-tree complex wavelet transform (DT-CWT). Finally, the variational approximation between different dynamic textures is measured using the Kuller-Leibler divergence (KLD) variational approximation. The effectiveness of the proposed method is verified by experimental evaluation of two popular benchmark texture databases (UCLA and DynTex++).
Web advertisement detection using Naive Bayes

Nowadays, with the development of the internet, more and more people find information on the websites. So the network promoters pretend to write advertising content on websites, advertising content will reduce the user's experiment if the users always find the advertisement instead of the content they need. It will take web managers a lot of time to review whether the content is advertisement or useful content. Even worse, when we daily use message applications, there are always groups full of advertisement messages. It takes a lot of time to manage the users and the content, so many managers just leave it and the groups will be worthless. Basically, when we try to separate useful content from advertisement, we use the keywords. For example, if the content contains keywords like promotion champion, sale, we prefer to define it as advertisement. On the other hand, if the content has keywords like weekend, dinner, we prefer to define it as useful content. This processing can be automated as a text classification problem with the help of machine learning algorithms: k-NN Algorithm, Naive Bayes Algorithm, support vector machine, decision tree, neural network [1][2][3][4][5][6]. This paper applies Naive Bayes to advertisement detection in web content [3].
Determining Temperature Extreme in Warri City, Niger-Delta Region, Nigeria

Climate change and global warming which is also known as a change in Earth’s overall climate or rising temperature have taken centre stage in international concerns, several fora and treaties have been observed with a view of stemming trend, in rising temperatures. This study evaluated ten years of maximum and minimum annual temperature of Warri in Nigeria between (2005 and 2015) to determine trends and identified extreme fluctuation in temperature. Data used for this study were sourced from the Nigerian Meteorological Agency’s Zonal Office, Warri. An objective method for determining temperature extreme has been used. Least square linear regression equation has been used to estimate temperature that would be equalled or surpassed 1%, 5% and 10% of the hours at any given location during the warmest and coldest months of the year. These equations are based on an index calculated from the three readily available parameters; the mean monthly temperature, the mean daily maximum temperature for the month and the mean daily minimum temperature for the month. The warmest month in Warri was March with a mean monthly temperature of 33.9 while the coldest month was July with mean monthly of 25.8.
Relative sensor registration with two‐step method for state estimation

State estimation suffers from some new challenging problems with a multi-platform multi-sensor observation system. An important problem for multisensor integration is that the data from the local sensors needs to be transformed into a common reference frame free of systematic bias or registration. In this study, the relative sensor registration problem is discussed. It aligns measurement from the global sensor with the local sensor under the assumptions that the global sensor is bias free and all biases reside with the local sensor. The traditional methods failed in the condition when attitude bias becomes large because the error caused by linearisation of rotation matrix increases with growing attitude bias. Motivated by this, a two-step method is established. By estimating the measurement bias through augmented extended Kalman filter in local sensor coordinate independent of attitude and location bias, and by introducing the unit quaternion method compute the attitude and location bias, the proposed method can avoid the problem the traditional methods faced. Simulation examples are provided to verify the proposed method by comparing with the existing linear least square algorithm.
Deep Learning Innovations in Recommender Systems

Recommender systems are one of the best choices to cope with the problem of information overload.These systems are commonly used in recent years help to match users with different items.As more data is available on the internet traditional methods suffer from challenges like accuracy and scalability.Deep learning a state of art machine learning method also achieve promising performance in the field of recommender system.In this study we provide an overview of traditional approaches their limitations and then discuss about the aspects of deep learning used in the recommender system domain to improve the accuracy in recommender system domains.These deep recommender systems can be used to understand the demands of users and improve the value in recommendations.
Multiagent Adversarial Inverse Reinforcement Learning

Learning to coordinate is a hard task for reinforcement learning due to a game-theoretic pathology known as relative overgeneralization. To help deal with this, we propose two methods which apply forms of imitation learning to the problem of learning coordinated behaviors. The proposed methods have a close connection to multiagent actor-critic models, and will avoid relative overgeneralization if the right demonstrations are given. We compare our algorithms with MADDPG, a state-of-the-art approach, and show that our methods achieve better coordination in multiagent cooperative tasks.
Cross-Domain Image Steganography Based on GANs

According to the embedding method of secret information, steganography can be divided into: cover modification, selection and synthesis. In view of the problem that the cover modification will leave the modification trace, the cover selection is difficult and the load is too low, this paper proposes a cross-domain image steganography scheme based on GANs, which combines with cover synthesis. In the case that the cover type is not given in advance, the proposed scheme is driven by secret message, selects material to build the encrypted carrier, and converts it into cross-domain image, which is mapped to the generative image space for transmission. This scheme is consistent with the idea of coverless information hiding and can effectively resist the detection of steganalysis algorithm. Experiments were carried out on the data set of CelebA, and the results verified the feasibility and security of the scheme.
Support for Long-lived BGP Graceful Restart

In this document we introduce a new BGP capability termed Long-livedGraceful Restart Capability so that stale routes can be retained fora longer time upon session failure. A well-known BGP communityLLGR_STALE is introduced for marking stale routes retained for alonger time. A second well-known BGP community, NO_LLGR, isintroduced to mark routes for which these procedures should not beapplied. We also specify that such long-lived stale routes be treatedas the least-preferred, and their advertisements be limited to BGPspeakers that have advertised the new capability. Use of thisextension is not advisable in all cases, and we provide guidelines tohelp determine if it is. We update RFC 6368 by specifying that theLLGR_STALE community must be propagated into, or out of, the pathattributes exchanged between PE and CE.
Security of Khudra Against Meet‐in‐the‐Middle‐Type Cryptanalysis

Khudra is a lightweight block cipher proposed in SPACE 2014. The cipher is designed for Field programmable gate array (FPGA) based platforms. In this paper, we introduce the first biclique attack on full Khudra in the single key setting, with time complexity of 278.3 encryptions. The time complexity can be further reduced if the post-whitening key is omitted. Furthermore, based on the bicliques constructed, Meet-in-the-middle(MITM) attack is applied to 15-round Khudra, and the best result of Khudra in terms of attacked rounds against MITM attack is achieved.
Cryptocurrency: Advantages and Disadvantages

[full article and abstract in Lithuanian; abstract in English]&#x0D; The main feature of the twenty-first century could be named as permanent transformation that covers all areas. Payments and money are developing and changing especially fast. Technological evolution and development have led to the emergence of new transactional techniques on the Internet, mobile phones, and credit cards. Various systems of payment / money transfer have considerably expanded payment options and possibilities. And such conditions have created opportunities for the emergence of a cryptocurrency. The purpose of the study is to investigate the cryptocurrency, its concept, advantages and disadvantages. Analysis of the scientific literature, systematization, comparison, interpretation and generalization of information research methods were used for the study. Evaluation of the reasons for the emergence of cryptocurrency let to generalise, that the process was mostly influenced by technological progress and the aspiration to find faster, cheaper and more confidential settlement tools. Cryptocurrency may be defined as digital money or property in electronical form. This currency may be characterized by properties of decentralization, uncontrollability and higher risk management. The most important advantages of using a cryptocurrency may be: the speed of settlements; cheaper financial services; additional investment opportunities. Disadvantages - unpredictable, fluctuating value; possible fraud cases; possibility to use cryptocurrency for illegal activities.
A 3D Virtual Sketching System Using NURBS Surfaces and Leap Motion Controller

The value of (semi-)automatically recognizing the meaningful regions on a Computer Aided Design (CAD) model is well documented and has been traditionally studied as Geometric feature recognition.However, the effective tagging of the regions, especially tagging with contextually meaningful (i.e.semantic) keywords, has been elusive.In this paper, we focus on the non-geometric aspects of the tagging problem and present a semantic tagging framework.Specifically, given the contextually augmented instances created from the unidentified regions on a CAD model, the framework tags the instances with semantic keywords defined in the semantic model.This will provide the direct link between a CAD model and semantic models, and thus allow high-level reasoning to support design and manufacturing tasks.The feasibility of the approach has been verified by applying the developed framework to tag the regions on turbine blade models.This paper concludes with the future challenges of the approach.
Stream Reasoning Agents: Blue Sky Ideas Track

Data streams are increasingly needed for different types of applications and domains, where dynamicity and data velocity are of foremost importance. In this context, research challenges raise regarding the generation, publication, processing, and discovery of these streams, especially in distributed, heterogeneous and collaborative environments such as the Web. Stream reasoning has addressed some of these challenges in the last decade, presenting a novel data processing paradigm that lays at the intersection among semantic data modeling, stream processing, and inference techniques. However, stream reasoning works have focused almost exclusively on architectures and approaches that assume an isolated processing environment. Therefore, they lack, in general, the means for discovering, collaborating, negotiating, sharing, or validating data streams on a highly heterogeneous ecosystem as the Web. Agents and multi-agent systems research has long developed principles and foundations for enabling some of these features, although usually under assumptions that require to be revised in order to comply with the characteristics of data streams. This paper presents a vision for a Web of stream reasoning agents, capable of sharing not only streaming data, but also processing duties, using collaboration and negotiation protocols, while relying on common vocabularies and protocols that take into account the high dynamicity of their knowledge, goals, and behavioral patterns.
The Heavy Burden of Thin Regulation: Lessons Learned from the SEC’s Regulation of Cryptocurrencies

The Trump administration has taken a strong stance against regulation. This includes not only ratcheting down the level of regulation and enforcement in general, but it also entails a strong dislike of the absolute number of regulations in existence. For example, within two weeks of taking office, President Trump issued his Executive Order on Reducing Regulation and Controlling Regulatory Costs, which provides in part: “Unless prohibited by law, whenever an executive department or agency... publicly proposes for notice and comment or otherwise promulgates a new regulation, it shall identify at least two existing regulations to be repealed.” In a certain regard, this policy of reducing the absolute number of regulations is laudable. The administrative state and the regulation associated with it have expanded massively since the first half of last century. A reduced volume of regulation means that lawyers and society at large can be better versed in their legal obligations and potentially be less encumbered in their activities. With that said, “thin regulation,” as it will be termed, does have its downsides, including potential gaps in regulation, improper regulatory coverage, due process issues relating to notice, legitimacy concerns, and increased risk of market collapse relating to the regulated subject matter.To understand the problems with thin regulation, the best place to begin is with an emerging issue and to explore how President Trump’s policy of thin regulation interfaces with it. To that end, the recent advent of cryptocurrencies offers an excellent case study of the dangers of thin regulation. Although the United States Securities and Exchange Commission (SEC) has undertaken efforts to act in this area, the gaps that exist illustrate the problems with thin regulation. Essentially, the SEC has tried to force cryptocurrencies into an existing regulatory structure that does not sufficiently mitigate the dangers that cryptocurrencies create. This essay explores the problems created by the lack of cohesive regulation in this area and demonstrates that the value of a thin regulatory approach is outweighed by the value of a well-reasoned, narrowly tailored approach — which I will term “bespoke regulation.”
Pelayanan bimbingan literasi dan sumber informasi perpustakaan bagi mahasiswa yang sedang menyusun tugas akhir

Introduction. There are various types of information sources and guidelines on information literacy at libraries. It is necessary to understand the types of information sources and guidelines on information literacy needed by students, particularly for those who are in the stage of writing their final assignments. The aims of this paper is to provide better understanding of the need of information sources for the final year students.Research Methods. This is qualitative descriptive research with 10 final year students in structured interviews as well as observations from the Indonesian Islamic University. Data analysis. Data from interviews and observations were analyzed using qualitative methods on the variables used as research subjects.Results and Discussions. The data showed that types of online and printed information sources include (a) textbooks; (b) journals; (c) proceedings and (d) final assignments (theses and dissertations). In terms of information literacy guidelines, they need some guideline to (a) access to information sources; (b) analyse information sources; (c) evaluate information sources and (d) use information sources.Conclusions and Recommendations. The library should provide several types of information sources and information literacy guidelines for students to prepare their final assignments. The paper also suggests that the Directorate of Indonesian Islamic University Library to consider better students’ information needs and continue with further similar research.
Optimal Value of Information Based Elicitation During Negotiation

Autonomous agents engaging in automatic negotiations on behalf of humans or institutions are usually assumed to have full knowledge of the utility function for the actors they represent. In many cases, these utility functions are difficult to know apriori for every possible outcome of the negotiation. Moreover, it may not be necessary for the agent to know the utility of outcomes that are never offered or considered during the negotiation. State-of-the-art approaches to utility elicitation during negotiation assume that the agent can ask questions from a predefined countable set to reduce its uncertainty about the utility function. This paper extends that body of work by lifting the countability assumption providing an optimal algorithm for selecting the best outcome and utility level about which to ask the actor. The paper reports the results of comparing the proposed algorithm with state-of-the-art algorithms using both synthetic and realistic negotiation scenarios. These evaluations support the applicability of the proposed approach.
Extension Results for Lipschitz Mappings

In this chapter we present various extension results for Lipschitz functions obtained by Kirszbraun, McShane, Valentine and Flett—the analogs of Hahn-Banach and Tietze extension theorems. A discussion on the corresponding property for semi-Lipschitz functions defined on quasi-metric spaces and for Lipschitz functions with values in a quasi-normed space is included as well.
Data Mining Using Clustering Algorithm as Tool for Poverty Analysis

Poverty in one place can be reduced or minimized if proper poverty alleviation programs are given to the community. In this study different clustering algorithms were evaluated using silhouette index to get the best clustering algorithm to group the households and analyze the poverty data. The k-means algorithm where k=3 outperformed DBSCAN and k-medoids with a silhouette of 0.308. The algorithm produced three groups or clusters and labelled as non-poor, near poor and poor. The result can help policy-makers formulate and implement poverty reduction policies and programs that are clear, reasonable, realistic, and enforceable.
Copyright Act, 1957: A Study with Reference to Selected Cases in India

The law of Copyright Act 1957 has been giving protection to literary, dramatic or musical works, artists, cinematograph film and sound record. Now days it has been challenging even in the field of research publications, thesis and patent. In this paper the Researchers focus on the laws of copyrights and libraries, also on the challenges and issues of copyright. Some of the cases related to the copyright are also discussed. Keywords: Copyright Laws, Library, Infringement of copyright, Intellectual Property, Licenses Cite this Article Vishwasrao S Mane, Namita Khot. Copyright Act, 1957: A Study with Reference to Selected Cases in India. Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 336s–341s.
BLENDED LEARNING IMPLEMENTATION IN TEACHING BIOLOGY IN UKRAINIAN SECONDARY SCHOOLS

Nowadays lots of modern educational technology tools are integrated to the educational process of a new Ukrainian school system.It is obviously that computers, digital technologies, multimedia, interactive boards, smartphones and tablets can help students actively engage in learning process and develop their cognitive, critical thinking and problem solving skills, improve their ability to manage emotions, cooperate and communicate not only with other secondary students but with school staff, teachers, coevals, and to strive for an improvement as well.These outcomes are fully consistent with the goal of Ukrainian state education that is described in the Law of Ukraine On Education (2017): "A goal of the education is comprehensive development of the human being as a personality and as the supreme value of the society, development of its talents, intellectual, creative and physical abilities, formation of values and competences necessary for successful self-realization, raising responsible citizens capable to make a conscious choice and channel their activities for the good of other people and the society, and enriching the intellectual, economic,
Skip Act Vectors: Integrating Dialogue Context into Sentence Embeddings

This paper compares several approaches for computing dialogue turn embeddings and evaluate their representation capacities in two dialogue act related tasks within a hierarchical Recurrent Neural Network architecture. These turn embeddings can be produced explicitely or implicitely by extracting the hidden layer of a model trained for a given task. We introduce skip-act, a new dialogue turn embeddings approach, which are extracted as the common representation layer from a multi-task model that predicts both the previous and the next dialogue act. The models used to learn turn embeddings are trained on a large dialogue corpus with light supervision, while the models used to predict dialog acts using turn embeddings are trained on a sub-corpus with gold dialogue act annotations. We compare their performances for predicting the current dialogue act as well as their ability to predict the next dialogue act, which is a more challenging task that can have several applicative impacts. With a better context representation, the skip-act turn embeddings are shown to outperform previous approaches both in terms of overall F-measure and in terms of macro-F1, showing regular improvements on the various dialogue acts.
Tangible Robotic Fleet Control

The use of multi-robot teams for field missions is increasing in number and scope, requiring command and control interfaces to be adapted to the operator's needs. Instead of working on interaction modalities that emerge from the engineering realm (screen, gestures or voice), we look at how the humanitarian and military logistics teams collaborate: using physical maps. In this demo, we present our command center, which consists of a swarm of small tabletop robots used to visualize and control a fleet of flying robots. To ensure the scalabilty and robustness of our control system, we leverage decentralized behaviors written in a swarm-specific programming language. We set an example scenario, where the operator must command the fleet to search an area for simulated features of interest using the tabletop robots over a map. The actions of the operator send the flying robots to individual waypoint targets. Meanwhile, the command center monitors the operator: if he is not alone, he may lack focus, and the fleet thus switches to an autonomous deployment mode until the operator's full attention is back.