Reviewerly/exHarmony huggingface dataset:
Large-Scale Video Classification with Feature Space Augmentation Coupled with Learned Label Relations and Ensembling

This paper presents the Axon AI’s solution to the 2nd YouTube-8M Video Understanding Challenge, achieving the final global average precision (GAP) of 88.733% on the private test set (ranked 3rd among 394 teams, not considering the model size constraint), and 87.287% using a model that meets size requirement. Two sets of 7 individual models belonging to 3 different families were trained separately. Then, the inference results on a training data were aggregated from these multiple models and fed to train a compact model that meets the model size requirement. In order to further improve performance we explored and employed data over/sub-sampling in feature space, an additional regularization term during training exploiting label relationship, and learned weights for ensembling different individual models.
Retrieval of Relevant Web Pages by a New Filtering Method

With the great mass of the pages managed through the world, and especially with the advent of the web, it has become more difficult to find the relevant pages after an interrogation. Furthermore, the manual filtering of the indexed web pages is a laborious task. A new filtering method of the annotated web pages (by a semantic annotation process) and the non-annotated web pages (retrieved from search engine Google) is then necessary to group the relevant web pages for the user. In this chapter, the authors first synthesize their previous work of the semantic annotation of web pages. Then, they define a new filtering method based on three activities. They also present their querying and filtering component of web pages; their purpose is to demonstrate the feasibility of our filtering method. Finally, the authors present an evaluation of this component, which has proved its performance for multiple domains, and they discuss the use of the extended Boolean retrieval method in the new filtering method.
Combining weighted curvelet accumulation with motion vector duty cycle for nonuniform video deblurring

Since object motion and camera shake can cause motion blur, there are some blurry frames in the captured videos by hand-held camera. In order to solve the above problem, we propose a nonuniform video deblurring method that combines weighted curvelet accumulation with motion vector duty cycle. In the proposed method, firstly we propose a weighted curvelet accumulation method that can synthesize the multiple adjacent frames in the frequency domain for estimating the initial latent sharp frame. Secondly, because the duty cycle has a close relation with the accuracy of the blur kernel, we propose a motion vector duty cycle estimation method by utilizing the inter-frame correlation information and the estimated initial latent sharp frame to improve the blur kernel accuracy. Finally, we build a novel nonblind video deblurring model by fully utilizing the spatiotemporal information and the estimated blur kernel for obtaining the deblurred frame. Experimental results on the numerous videos show that the proposed method achieves the state-of-the-art results either in subjective vision or in objective evaluation.
Cloud Provider's Response to Multiple Models of Attack Behaviors

Background: User behavior models have been widely used to simulate attack behaviors in the security domain. We revised all patents related to response to attack behavior models. How to decide the protected target against multiple models of attack behaviors is studied. Methods: We utilize one perfect rational and three bounded rational behavior models to simulate attack behaviors in cloud computing, and then investigate cloud provider’s response based on Stackelberg game. The cloud provider plays the role of defender and it is assumed to be intelligent enough to predict the attack behavior model. Based on the prediction accuracy, two schemes are built in two situations. Results: If the defender can predict the attack behavior model accurately, a single-objective game model is built to find the optimal protection strategy; otherwise, a multi-objective game model is built to find the optimal protection strategy. Conclusion: The numerical results prove that the game theoretic model performs better in the corresponding situation.
Improved Programming-Language Independent MapReduce on Shared-Memory Systems

The MapReduce programming paradigm is a prominent model for expressing parallel computations, especially in the context of data processing of vast data sets. However, modern data processing runtimes, implementing the MapReduce programming paradigm, do not generally support the use of arbitrary programming languages. Access to programming-language independent data processing can offer great value to organizations as it enables leveraging existing programming language expertise. This paper introduces XRT, an open-source programming-language independent MapReduce runtime for shared-memory systems. We introduce a number of current optimizations and extensions to the state-of-the-art. Furthermore, this paper presents the first comparative benchmark between XRT and many popular MapReduce runtimes in order to highlight the performance capabilities of programming-language independent data processing.
The Implementation of Modern Information Technologies in Educational Fields

As known to all, we are living in an information society today. Learners can acquire knowledge, skill, and ability by participating in teaching, training and learning systems. As the basis of teaching, training, and learning system, the management system is significantly important in the management of online materials and intercommunion between students and teachers. In order to construct more effective systems of management, virtual organizations should be considered with latticed structure, which can easily adapt to external environment changing and may even inspire more innovations in the fields of education.
Finding Ordinary Cube Variables for Keccak-MAC with Greedy Algorithm

In this paper, we introduce an alternative method to find ordinary cube variables for Keccak-MAC by making full use of the key-independent bit conditions. First, we select some potential candidates for ordinary cube variables by properly adding key-independent bit conditions, which do not multiply with the chosen conditional cube variables in the first two rounds. Then, we carefully determine the ordinary cube variables from the candidates to establish the conditional cube tester. Finally, based on our new method to recover the 128-bit key, the conditional cube attack on 7-round Keccak-MAC-128/256/384 is improved to $$2^{71}$$ and 6-round Keccak-MAC-512 can be attacked with at most $$2^{40}$$ calls to 6-round Keccak internal permutation. It should be emphasized that our new approach does not require sophisticated modeling. As far as we know, it is the first time to clearly reveal how to utilize the key-independent bit conditions to select ordinary cube variables for Keccak-MAC.
Information Literacy

This article discusses the need and importance of information literacy as an essential component in information society. The article highlights the role of information and information technology as well as the relationship between information and literacy. This article also studies the impact of ICT in rural areas and examines the awareness of the term like, information, information literacy, computer literacy etc. in the rural communities in Manipur, India. Further, it elaborates the need for information literacy and challenges faced by the rural communities in the global information environment.
Changes in Online Subjectivity

Human subjectivity is deeply affected by the environment as mediated by the Internet. As users within this environment, people are not only affected by their entrance into it, but their subjectivity evolves along with the trends of online networking. The subjectivity of people is affected by (their networking with) human and artificial users of the environment and the possibility of influencing user's subjectivity is also given by other specific aspects of the environment. This chapter focuses on three areas: artificial users, subject and its fragmentarization, and multimodal interaction. Findings in conceptualizing change in terms of subjectivity are based within the poststructuralist philosophical tradition. There is applied the phenomenon known as the “death of the author,” proclaimed in late 1960s, to the situation of the contemporary user to uncover changes in online subjectivity. The chapter also presents examples of implications of such changes at the level of interactions and distribution of messages and the role of artificial users in these processes within online marketing.
Classifying Adaptive Sustainable Flood Retention Basins

The Sustainable Flood Retention Basin (SFRB) concept provides a rapid assessment technique for impoundments. A previous version of the SFRB survey method developed by the author recommends gathering of over 40 variables to characterize an SFRB. The objective of this chapter is to explore the correlation among these variables and find the most important variables to represent an SFRB. Three feature selection techniques were applied on the SFRB dataset to identify the importance of the variables in terms of classification accuracy. Four benchmark classifiers were subsequently used to identify the optimal number of variables. Experimental results indicate that our proposed approach provides a simple, rapid, and effective framework for variable selection and SFRB classification. Only nine important variables are sufficient to accurately classify SFRB. Finally, six typical cases were studied to verify the performance of the identified nine variables on different SFRB types.
Multi-scales Image Denoising Method Based on Joint Confidence Probability of Coefficients

Background: It is a classic problem that we estimate the original coefficient from the known coefficient disturbed with noise. Methods: This paper proposes an image denoising method which combines the dual-tree complex wavelet with good direction selection and translation invariance. Firstly, we determine the expression of probability density function through estimating the parameters by the variance and the fourth-order moment. Secondly, we propose two assumptions and calculate the joint confidence probability of original coefficient under the situation that the disturbed parental and present coefficients from neighborhood scale are known. Finally, we set the joint confidence probability as shrinkage function of coefficient for implementing the image denoising. Results: The simulation experiment results show that, compared to these traditional methods, this new method can reserve more detail information. Conclusion: Compared to the current methods, our novel algorithm can remove the most noise and reserve the detail texture in denoising results, which can make better visualization. In addition, our algorithm also shows advantage in PSNR.
Cryptomorphic topological structures: A computational, relation-algebraic approach

In this paper we present an approach to pointless topology that is based on the study of the membership or is-element-of relation within a typed or categorical version of the component-free calculus of relations. This allows us to study several other approaches to topology, including point-set topology, on an abstract and component-free level. In particular, we will show that topologies defined by open sets, closed sets, a family of neighbourhood systems, a topological kernel-mapping, a Kuratowski closure-mapping, or a topological Aumann contact relation are cryptomorphic concepts, i.e., each concept can bijectively be transformed into any other of these concepts. All transformations are specified via relation-algebraic expressions which, in case of set-theoretic relations (that is, in case of point-set topology) can immediately be executed by the specific purpose computer algebra system RelView.
Evidential Learning on Web Search Queries Disambiguation for Active Strategic Decision Making

The web search engines are perhaps the most significant of all software systems. True, the vast information needs of the users would not satisfy without a way to represent the data and provide the interface components. The fast growth of the information world and the internet saturation into all fields has increased the importance of the problem of information access. In pursuance of communication being made with a web search system, the user must express his or her needs in the form of queries. However, due to the lack of domain knowledge and the limitation of natural language such as synonym and polysemy, many system users cannot formulate their needs into effective queries. In this chapter, the authors attempt to give detailed description of ambiguity concept, search queries, and then disambiguation process with respect to different types. Disambiguating the search intent and improving the accuracy of resulting information is a crucial issue in the domain of web search systems, especially when most of users are unable to clearly express their information needs.
Perhaps This Cannot Be Anything (on Exercises in Text Production and Critical Typography, Which Make Use of Machine-Made Errors in Processes of Visual and Semantic Translation)

Activities of translation often look to shift meaning or form from one state to another and, as a consequence, a range of potential transformations can often take place. This essay discusses the material traces of linguistic invention as a means to examine the disruptive and transformative possibilities of translation as a distinct ‘designerly’ activity. Critical design is employed as a method through which ideas and practices of translation can be applied through automatic or machine-derived methods which seek to embrace the inherent mistakes, errors or unintended consequences of such approaches. The results of these activities raise questions relating to a particular value of critical approaches within design and specifically in terms of the characteristics of outcomes that might result from such an enquiry.
Counterfactually Fair Prediction Using Multiple Causal Models

In this paper we study the problem of making predictions using multiple structural causal models defined by different agents, under the constraint that the prediction satisfies the criterion of counterfactual fairness. Relying on the frameworks of causality, fairness and opinion pooling, we build upon and extend previous work focusing on the qualitative aggregation of causal Bayesian networks and causal models. In order to complement previous qualitative results, we devise a method based on Monte Carlo simulations. This method enables a decision-maker to aggregate the outputs of the causal models provided by different agents while guaranteeing the counterfactual fairness of the result. We demonstrate our approach on a simple, yet illustrative, toy case study.
Nurturing Collaborative Networks of Mobile Learning Researchers and Practitioners

In this article, we present the development of a framework for supporting and facilitating collaborative networks of reflective practice using mobile social media. Developed throughout a two-year collaborative mobile learning project #NPF14LMD, the framework has subsequently been used to support two wider international networks of mobile learning researchers and practitioners. The #NPF14LMD project was a national project comprised of three universities and three polytechnics across New Zealand. One of the goals of the #NPF14LMD project was to create a collaborative network of practice across the six institutions participating in the project. The network provided a support and communication structure linking the six institutional communities of practice, enabling sharing of their experiences and a sense of belonging to a wider national and international community. This article outlines the use of mobile social media to facilitate the #NPF14LMD network, and the subsequent application of this framework to support two international networks.
Managing and Shaping Change in International Projects

Companies are either international by nature, either their workforce is, or business dynamics and agile optimization create an extended web making it international. This obliges managers to gear up for the needs of this evolving DNA. This is especially the case in strategy critical project portfolio contexts focusing on organizational change, process transformation, or roll-out of headquarter-driven products/services. This chapter will address the required hard and soft assets: integrating the cultural essence and maturity of the ecosystem, combining experience with a pragmatic approach, and being dedicated to continuous shaping of collaborations. Additional focus will be set on managing in the digital age. To give more depth to the tangible value, different real-life cases will be integrated. Together, the theoretical insights and the empirical examples will offer a big picture view that will benefit the management of hybrid portfolios in geographically blended environments.
Environmental Adaption Method

This article describes how optimization is a process of finding out the best solutions among all available solutions for a problem. Many randomized algorithms have been designed to identify optimal solutions in optimization problems. Among these algorithms evolutionary programming, evolutionary strategy, genetic algorithm, particle swarm optimization and genetic programming are widely accepted for the optimization problems. Although a number of randomized algorithms are available in literature for solving optimization problems yet their design objectives are same. Each algorithm has been designed to meet certain goals like minimizing total number of fitness evaluations to capture nearly optimal solutions, to capture diverse optimal solutions in multimodal solutions when needed and also to avoid the local optimal solution in multi modal problems. This article discusses a novel optimization algorithm named as Environmental Adaption Method (EAM) foable 3r solving the optimization problems. EAM is designed to reduce the overall processing time for retrieving optimal solution of the problem, to improve the quality of solutions and particularly to avoid being trapped in local optima. The results of the proposed algorithm are compared with the latest version of existing algorithms such as particle swarm optimization (PSO-TVAC), and differential evolution (SADE) on benchmark functions and the proposed algorithm proves its effectiveness over the existing algorithms in all the taken cases.
Local Search Strategy Embedded ABC and Its Application in Cost Optimization Model of Project Time Schedule

This article describes how artificial bee colony (ABC) is a promising metaheuristic algorithm, modeled on the intelligent forging behavior of honey bees. ABC takes its inspiration from natural honey bees. In ABC the colony of bees is generally alienated into three groups namely scout, employed and onlooker bees that participates in getting optimal food sources (solutions). With an edge over similar metaheuristic algorithms in solving optimization problems ABC suffers with bad exploitation (local search) capability, however excels in exploration (global search) capability. In order to balance both the aforesaid capabilities, this article embeds the local search strategy in the basic structure of ABC. The proposed scheme is named as LS-ABC. The efficiency of the proposed scheme has been tested and simulated results are compared with state-of-art algorithms over 12 benchmark functions. Also, LS-ABC has been validated to solve cost optimization model of project time schedule. The simulated results are compared with state-of-art algorithms.
A hybrid CAD system design for liver diseases using clinical and radiological data

Abstract Computer-aided diagnostic (CAD) systems for the characterization of different datasets for different diseases have the potential to assist radiologists and clinicians in deducing whether a patient is suffering from a particular disease through the processing of clinical data by different machine learning algorithms. In the present study, three different CAD systems have been designed for the diagnosis of benign and malignant liver tumors, namely, (a) a CAD system design based on clinical data (benchmark Liver disorders, BUPA dataset created by University of California, Irvine), (b) a CAD system design based on radiological data, that is, ultrasound images with benign and malignant liver tumors, and (c) A hybrid CAD system design based on both the clinical and radiological data. The SSVM classifier has been exhaustively used for the classification task.
Significance of Epoch Identification Accuracy in Prosody Modification for Effective Emotion Conversion

Estimating the accurate pitch marks for prosody modification is an essential step in the epoch based time and pitch scale (prosody) modification of a given speech. In epoch based prosody modification, the perceptual quality of the time and pitch scale modified speech depends on the accuracy with which glottal closure instants (epochs) are estimated. The objective of the present work is to improve the perceptual quality of the prosody modified speech by accurately estimating the epochs location. In the present work the effectiveness of variational mode decomposition (VMD) in spectral smoothing and wavelet synchrosqueezing transform (WSST) in time-frequency sharpening of a given signal is exploited for refining the zero frequency filtering (ZFF) method which is one of the simple and popular epoch extraction method. The proposed refinements to the ZFF method found to provide improved epoch estimation performance on emotive speech utterances where the conventional ZFF method show severe degradation due to rapid pitch variations. Improved mean opinion scores are obtained based on the subjective evaluation tests performed on the prosody modified speech with the epochs estimated using the refined ZFF method. The reason for improved perceptual quality in the prosody modified speech is the better identification accuracy of the estimated epochs using the proposed method as compared to the conventional ZFF method in the case of emotive speech signals.
Visual vehicle detection scheme on low-powered embedded GPU

Real-time vehicle detection is one of the challenging problems for automotive and autonomous driving applications. Object detection using Deformable Parts Model (DPM) proved to be a promising approach providing higher detection accuracy. But the baseline DPM scheme spends 98% of its execution time in loop processing thus highlighting the drawback of higher computational cost for real time applications. In this paper, we have proposed a real time vehicle detection scheme for a low-powered embedded Graphics Processing Unit (GPU). The proposed scheme is based upon DPM approach using CUDA programming with different parallelization and loop unrolling schemes to reduce computational cost of DPM. Three loop unrolling schemes i.e. loosely unrolled, tightly unrolled and hybrid unrolled is proposed and implemented on two different datasets. Finally, we provided an optimal solution for vehicle detection with minimum execution time without having any impact on vehicle detection accuracy. We achieved a speedup of 3x to 5x as compared to state-of-the-art GPU implementation and 30x as compared to baseline CPU implementation of DPM on a low-powered automotive-grade embedded computing platform which features a Tegra K1 System on Chip (SOC), thus getting advantage of improved efficiency through parallel computation of CUDA.