Reviewerly/exHarmony huggingface dataset:
Refined video segmentation through global appearance regression

To achieve accurate segmentation in unconstrained videos, we propose a novel segmentation framework based on a two-stream deep convolution network. Our algorithm exploits the object’s robust pixel-level features within all the video frames and generates foreground likelihood maps with sufficient details. At first, a two-stream video segmentation network using multiple hierarchical features is designed to generate initial segmentation masks. Then, all initial segmentation masks and original corresponding images are collected to learn an appearance model by the least-square regression method. The model computes appearance likelihood map for every image. Finally, pairs of initial segmentation masks and appearance likelihood maps are fused by a proposed fusion network to generate final high-quality segmentation maps. Experiments on the challenging dataset DAVIS verify the effectiveness of our appearance regression and demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms.
What Should We Teach in Information Retrieval?

Modern Information Retrieval (IR) systems, such as search engines, recommender systems, and conversational agents, are best thought of as interactive systems. And their development is best thought of as a two-stage development process: offline development followed by continued online adaptation and development based on interactions with users. In this opinion paper, we take a closer look at existing IR textbooks and teaching materials, and examine to which degree they cover the offline and online stages of the IR system development process. We notice that current teaching materials in IR focus mostly on search and on the offline development phase. Other scenarios of interacting with information are largely absent from current IR teaching materials, as is the (interactive) online development phase. We identify a list of scenarios and a list of topics that we believe are essential to any modern set of IR teaching materials that claims to fully cover IR system development. In particular, we argue for more attention, in basic IR teaching materials, to scenarios such as recommender systems, and to topics such as query and interaction mining and understanding, online evaluation, and online learning to rank.
Epistemic foundations for set-algebraic representations of knowledge

This paper formalizes an informal idea that an agent’s knowledge is characterized by a collection of sets such as a σ-algebra within the framework of a state space model. The paper fully characterizes why the agent’s knowledge takes (or does not take) such a set algebra as a σ-algebra or a topology, depending on logical and introspective properties of knowledge and on the underlying structure of the state space. The agent’s knowledge is summarized by a collection of events if and only if she can only know what is true, she knows any logical implication of what she knows, and she is introspective about what she knows. In this case, for any event, the collection that represents knowledge has the maximal event included in the original event. When the underlying space is a measurable space, the collection becomes a σ-algebra if and only if the agent is additionally introspective about what she does not know.
A probabilistic topic model using deep visual word representation for simultaneous image classification and annotation

Researches have shown that holistic examination of an image provides better understanding of the image compared to separate processes each devoted to a single task like annotation, classification or segmentation. During the past decades, there have been several efforts for simultaneous image classification and annotation using probabilistic or neural network based topic models. Despite their relative success, most of these models suffer from the poor visual word representation and the imbalance between the number of visual and annotation words in the training data. This paper proposes a novel model for simultaneous image classification and annotation model based on SupDocNADE, a neural network based topic model for image classification and annotation. The proposed model, named wSupDocNADE, addresses the above shortcomings by using a new coding and introducing a weighting mechanism for the SupDocNADE model. In the coding step of the model, several patches extracted from the input image are first fed to a deep convolutional neural network and the feature vectors obtained from this network are coded using the LLC coding. These vectors are then aggregated in a final descriptor through sum pooling. To overcome the imbalance between the visual and annotation words, a weighting factor is considered for each visual or annotation word. The weights of the visual words are set based on their frequencies obtained from the pooling method and the weights of the annotation words are learned from the training data. The experimental results on three benchmark datasets show the superiority of the proposed model in both image classification and annotation tasks over state-of-the-art models.
Testing for forbidden order patterns in an array

A sequence contains a pattern , that is, a permutations of [ k ], iff there are indices i 1 &lt; … &lt; i k , such that f ( i x ) &gt; f ( i y ) whenever π ( x ) &gt; π ( y ). Otherwise, f is π ‐free. We study the property testing problem of distinguishing, for a fixed π , between π ‐free sequences and the sequences which differ from any π ‐free sequence in more than ϵ n places. Our main findings are as follows: (1) For monotone patterns, that is, π = ( k , k − 1,…,1) and π = (1,2,…, k ), there exists a nonadaptive one‐sided error ϵ ‐test of query complexity. For any other π , any nonadaptive one‐sided error test requires queries. The latter lower‐bound is tight for π = (1,3,2). For specific it can be strengthened to Ω( n 1 − 2/( k + 1) ). The general case upper‐bound is O ( ϵ −1/ k n 1 − 1/ k ). (2) For adaptive testing the situation is quite different. In particular, for any there exists an adaptive ϵ ‐tester of query complexity.
Sifting through the ashes: Amazon Fire TV stick acquisition and analysis

The Amazon Fire TV Stick is a popular device that is the centre of entertainment for many homes. Its collection of functions and features generates a considerable amount of data, giving this device the potential to be included in a multiple investigations. Highlighting a clear requirement for a forensic analysis of the device. Previous research of smart entertainment devices focuses on the larger areas of the market including Smart TV's, smart speakers and smart watches. All have provided potential forensic artefacts that can be used in investigations. However, data is often acquired using methods that can compromise the forensics of the data. An Amazon Fire TV Stick was populated with data following a methodology that captured the multiple uses of the device. A chip off acquisition method was then applied to acquire a forensic image. Analysis demonstrated there were a number of artefacts recoverable relating to the system, users and Kodi. The majority of the relevant artefacts identified were located in SQLite3 databases and XML files.
Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering

In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction. False positives plague the fraud prediction industry. It is estimated that only 1 in 5 declared as fraud are actually fraud and roughly 1 in every 6 customers have had a valid transaction declined in the past year. To address this problem, we use the Deep Feature Synthesis algorithm to automatically derive behavioral features based on the historical data of the card associated with a transaction. We generate 237 features (>100 behavioral patterns) for each transaction, and use a random forest to learn a classifier. We tested our machine learning model on data from a large multinational bank and compared it to their existing solution. On an unseen data of 1.852 million transactions, we were able to reduce the false positives by 54% and provide a savings of 190K euros. We also assess how to deploy this solution, and whether it necessitates streaming computation for real time scoring. We found that our solution can maintain similar benefits even when historical features are computed once every 7 days.
Active learning in automated text classification: a case study exploring bias in predicted model performance metrics

Machine learning has emerged as a cost-effective innovation to support systematic literature reviews in human health risk assessments and other contexts. Supervised machine learning approaches rely on a training dataset, a relatively small set of documents with human-annotated labels indicating their topic, to build models that automatically classify a larger set of unclassified documents. “Active” machine learning has been proposed as an approach that limits the cost of creating a training dataset by interactively and sequentially focussing on training only the most informative documents. We simulate active learning using a dataset of approximately 7000 abstracts from the scientific literature related to the chemical arsenic. The dataset was previously annotated by subject matter experts with regard to relevance to two topics relating to toxicology and risk assessment. We examine the performance of alternative sampling approaches to sequentially expanding the training dataset, specifically looking at uncertainty-based sampling and probability-based sampling. We discover that while such active learning methods can potentially reduce training dataset size compared to random sampling, predictions of model performance in active learning are likely to suffer from statistical bias that negates the method’s potential benefits. We discuss approaches and the extent to which the bias resulting from skewed sampling can be compensated. We propose a useful role for active learning in contexts in which the accuracy of model performance metrics is not critical and/or where it is beneficial to rapidly create a class-balanced training dataset.
Internet of Everything (IoE)

The internet of everything (IoE) envelopes the internet of things (IoT) that was simply focusing primarily on machine-to-machine sensor-based smart device communications. The IoE expands to include people and processes in a much more comprehensive scope. The internet of everything is expanding our ability to collect massive amounts of data for comprehensive analysis to achieve a level of understanding not previously possible. Since the internet of everything (IoE) has such a strong focus on collecting and analyzing data using smart sensor-enabled devices, eye tracking data is a perfect match. Eye tracking and other biometric sensor-based data can be collected and analyzed locally in real time through fog/edge computing or cloud-based big data analytics.
Fast algorithms for mining maximal erasable patterns

Since the problem of mining erasable itemsets was identified in 2009, many algorithms have been proposed to improve mining time and/or memory usage. However, algorithms for mining maximal erasable itemsets (MaxEIs) have not been developed, and this article therefore focuses on this problem. Firstly, a GenMax-based algorithm (GenMax-EI) is developed as a baseline algorithm. Secondly, a proposition is developed for fast checking of whether or not an erasable itemset is maximal, and based on this proposition, we develop an algorithm entitled Flag-GenMax-EI for the fast mining of MEIs. Finally, a second proposition for the fast pruning of non-MaxEIs is also developed; based on this proposition, we propose an algorithm entitled PE-GenMax-EI for mining MaxEIs.
Top-Level Secure Certificateless Signature Scheme in the Standard Model

In order to overcome the key escrow problem, Al- Riyami and Paterson introduced the concept of certificateless public key cryptography (CL-PKC) in 2003. CL-PKC requires neither public key certification nor the key escrow problem. After that, CL-PKC has been widely applied. In 1991, Girault defined three security levels of the key generation center (KGC), where the higher level of KGC means the stronger security of the system. Recently, lots of certificateless signature schemes and their security models have been presented. However, there is no certificateless signature scheme proposed in the literature that achieves the property of Girault's level-3 security without random oracles. In view of aforementioned issues, we propose a new construction of certificateless signature scheme. The proposed certificateless signature scheme is provably secure in the standard model and satisfies Girault's level-3 security. The security of the proposed scheme is based on the hardness of generalized computational Diffie-Hellman and many Diffie-Hellman problems.
Recognition Over Encrypted Faces

Neural Networks (NN) are today increasingly used in Machine Learning where they have become deeper and deeper to accurately model or classify high-level abstractions of data. Their development however also gives rise to important data privacy risks. This observation motives Microsoft researchers to propose a framework, called Cryptonets. The core idea is to combine simplifications of the NN with Fully Homomorphic Encryptions (FHE) techniques to get both confidentiality of the manipulated data and efficiency of the processing. While efficiency and accuracy are demonstrated when the number of non-linear layers is small (e.g. 2), Cryptonets unfortunately becomes ineffective for deeper NNs which let the privacy preserving problem open in these contexts. This work successfully addresses this problem by combining several new ideas including the use of the batch normalization principle and the splitting of the learning phase in several iterations. We experimentally validate the soundness of our approach with a neural network with 6 non-linear layers. When applied to the MNIST database, it competes with the accuracy of the best non-secure versions, thus significantly improving Cryptonets. Additionally, we applied our approach to secure a neural network used for face recognition. This problem is usually considered much harder than the MNIST hand-written digits recognition and can definitely not be addressed with a simple network like Cryptonets. By combining our new ideas with an iterative (learning) approach we experimentally show that we can build an FHE-friendly network achieving good accuracy for face recognition.
Biometric Authentication Techniques and E-Learning

The primary goal of this chapter is to answer online exam frameworks by utilizing face acknowledgment to verify students for going to an online exam. A strategy in light of the utilization of neural systems to validate individuals' computerized unique mark framework for e-learning is present. This chapter centers around breaking down and contrasting the distinctive facial verification frameworks to confirm the understudies when they utilize e-learning stages, itemizing the expenses and the highlights of each structure recorded. Biometrics is a sensible verification used as a type of distinguishing proof and access control. It is additionally used to distinguish people in bunches that are under observation. Biometric identifiers are then particular quantifiable qualities used to mark and portray people. Biometric authenticators are as often as possible named as conduct and additionally physiological attributes. Physiological qualities are identified with the state of the body. In this chapter, the essential focus is on the distinctive biometrics and their applications.
Additive codes over Galois rings

Let S = GR ( p e , m ) be a Galois ring of characteristic p e and cardinality p e m . An additive code over S of length n is a subgroup of S n under addition. In this paper, we study additive codes over S . We introduce a correspondence between linear codes over Z p e and additive codes over S and we describe additive codes over S by the structure of linear codes over Z p e . In particular, we find the generator matrix and the number of additive codes over S , and we determine some classes of MDR additive codes over S . Among other results, permutation equivalent additive codes and decomposable additive codes are described. Also we prove MacWilliams identity and Delsarte theorem for additive codes over S .
Improved Cohort Intelligence—A high capacity, swift and secure approach on JPEG image steganography

In the recent high level of information security was attained by combining the concepts of cryptography, steganography along with the nature inspired optimization algorithms. However, in today's world computational speed plays a vital role for the success of any scientific method. The optimization algorithms, such as cohort Intelligence with Cognitive Computing (CICC) and Modified-Multi Random Start Local Search (M-MRSLS) were already implemented and applied for JPEG image steganography for 8 × 8 as well as 16 × 16 quantization table, respectively. Although results were satisfactory in terms of image quality and capacity, the computational time was high for most of the test images. To overcome this challenge, the paper proposes modified version of cohort intelligence (CI) algorithm referred to as Improved Cohort Intelligence (CI). The Improved CI algorithm was considered as a cryptography technique and implemented to generate optimized cipher text. Improved CI algorithm was further employed for JPEG image steganography to propose a reversible data hiding scheme. Experimentation was done on grey scale image, of size 256 × 256; both for 8 × 8 and 16 × 16 quantization table. Results validation of the proposed work exhibited very encouraging improvements in the computational cost.
QoS and performance metrics for container-based virtualization in cloud environments

Current cloud deployments heavily depend on hypervisor-based virtualizations. The overarching characteristics of Docker and containerization have given them a momentum in their widespread adoption recently as alternatives for their counterparts. However, little research has been done for comparing the QoS of both technologies, thus leaving the domain without widely accepted performance metrics. Aiming at informing the decision of the best fit in a specific cloud deployment, we have designed performance metrics that compare the performance of both designs in an in-house cluster deployed by using OpenStack. We focus on well-established representatives as baselines, including KVM from the hypervisor-based side, LXD from the container-based side in addition to Docker. Our results show that containerization is not a predominant fit-all solution that can always replace hypervisors for all cluster deployment and application scenarios. It can instead be thought of as a complementary solution to use for specific application scenarios that are constrained with conditions that are solved by containerization merits.
Development of an advection model for solar forecasting based on ground data. Part II: Verification of the forecasting model over a wide geographical area

A new spatiotemporal forecasting model for photovoltaic output power, outlined in the first paper, is validated herein against irradiance measurements. Irradiance measurements were first interpolated onto a grid. By repeating this process over several time steps, cloud motion was visualized, and the velocity of the clouds was estimated. Using these estimations of cloud velocity, the cloud motion for a future time interval was simulated using advection-diffusion equations and the average irradiation over a target region was estimated. The model was applied to a 170 km × 60 km region in northern Kyushu, Japan. Predictions of the transients for average irradiation were in good agreement with ground measurements. Simulation of the distribution of irradiation within the photovoltaic system also agreed well with ground measurements. This approach suggests an additional function for photovoltaic power systems, not only as a power source but also as a sensor for weather forecasting. This application provides a clear incentive to install more photovoltaic power systems.
Automated Early Leaderboard Generation from Comparative Tables

A leaderboard is a tabular presentation of performance scores of the best competing techniques that address a specific scientific problem. Manually maintained leaderboards take time to emerge, which induces a latency in performance discovery and meaningful comparison. This can delay dissemination of best practices to non-experts and practitioners. Regarding papers as proxies for techniques, we present a new system to automatically discover and maintain leaderboards in the form of partial orders between papers, based on performance reported therein. In principle, a leaderboard depends on the task, data set, other experimental settings, and the choice of performance metrics. Often there are also tradeoffs between different metrics. Thus, leaderboard discovery is not just a matter of accurately extracting performance numbers and comparing them. In fact, the levels of noise and uncertainty around performance comparisons are so large that reliable traditional extraction is infeasible. We mitigate these challenges by using relatively cleaner, structured parts of the papers, e.g., performance tables. We propose a novel performance improvement graph with papers as nodes, where edges encode noisy performance comparison information extracted from tables. Every individual performance edge is extracted from a table with citations to other papers. These extractions resemble (noisy) outcomes of ‘matches’ in an incomplete tournament. We propose several approaches to rank papers from these noisy ‘match’ outcomes. We show that our ranking scheme can reproduce various manually curated leaderboards very well. Using widely-used lists of state-of-the-art papers in 27 areas of Computer Science, we demonstrate that our system produces very reliable rankings. We also show that commercial scholarly search systems cannot be used for leaderboard discovery, because of their emphasis on citations, which favors classic papers over recent performance breakthroughs. Our code and data sets will be placed in the public domain.
Design and implementation of in-situ human-elephant conflict management system

Elephant play a major role in maintaining the ecosystem. Elephants move out of their corridor in search of food and water, result in rise of human-elephant conflicts. Human-Elephant conflict arises in different form such as destruction of field by elephants, elephants are runover by train, elephant s getting electrocuted etc. Prevention of elephants entering into human living areas, will reduce the human-elephant conflict to a greater extent. In this paper, performance of conventional image processing techniques, custom Convolutional Neural Network (CNN) architecture and transfer learning based CNN architectures is investigated in the context of human-elephant conflict management system. On identification of elephant from the acquired video feed, the sounds (humming of Bee, Tiger growls) are generated to mitigate the progress of elephant into human living areas. It is observed that the convolutional neural networks produced a higher accuracy or prediction rate compared to conventional image processing technique. It is also observed that, VGG16 CNN model produced an accuracy of 94% with an average computation time varying between 1.5 to 2.1 s. For real time implementation, SqueezeNet CNN model is used because of its lower computation time (varying between 0.02 to 0.05 s) and moderate accuracy of 92.67%.