Reviewerly/exHarmony huggingface dataset:
Security analysis and new models on the intelligent symmetric key encryption

Data protection is achieved in modern cryptography by using encryption. Symmetric key cryptography is mainly responsible for the actual user data protection in various network protocols such as SSL/TLS and so on. The design of such encryption algorithms have always been one of the most important research targets, where heavy cryptanalysis works have been performed to evaluate the security margin. As a result, the research community is busy with fixing the security flaws based on the cryptanalysis results. Recently, the idea of building the automatic security protection scheme based on the neural network has been proposed. The encryption algorithm, which is a neural network is instead constructed by machine during the learning stage in an adversarial environment. This is a totally different approach compared with our current design principle, and could potentially change our understanding about how the (symmetric key) encryption works and what is the security requirement for the scheme. In this paper, we investigate the security of the underlined scheme which remains unexploited based on several statistical models. And furthermore, we strengthen the automatic encryption schemes by introducing much stronger adversaries. Our results showed that the security solutions based on the advanced deep learning techniques may start to play an important role in the future related directions.
Factors associated with teachers discussing and intervening against homophobic language

Students report that teachers often do not intervene against homophobic language. Among 283 teachers in 16 Norwegian schools, several factors distinguished which teachers reported more consistently intervening and more frequently discussing homophobic language with students. Women, but not men, who more strongly believed that homophobic language should not be allowed and who believed it was harmful reported more consistent intervention. Women and men who reported greater self-efficacy to intervene reported more consistent intervention. In a second model, teachers who reported receiving education on homophobic bullying and who reported greater self-efficacy to intervene more frequently discussed homophobic language with students.
Improving LSTM CRFs using character-based compositions for Korean named entity recognition

Standard approaches to named entity recognition (NER) are based on sequential labeling methods, such as conditional random fields (CRFs), which label each word in a sentence and extract entities from them that correspond to named entities. With the extensive deployment of deep learning methods for sequential labeling tasks, state-of-the-art NER performance has been achieved on long short-term memory (LSTM) architectures using only basic features. In this paper, we address Korean NER tasks and propose an extension of a bidirectional LSTM CRF by investigating character-based representation. Our extension involves deploying a hybrid representation using ConvNet and LSTM for the sequential modeling of characters, namely a character-based LSTM-ConvNet hybrid representation. Using morphemes as processing units for bidirectional LSTM, we apply a proposed hybrid representation composed of morpheme vectors. Experimental results showed that the proposed LSTM-ConvNet hybrid representation yielded improvements over each single representation on standard Korean NER tasks.
Optimizing dynamic survivability and security of replicated data in cloud systems under co-residence attacks

Utilizing the virtualization technology, multiple virtual machines (VMs) can be created on a single physical server for different tasks, enabling cost-effective resource sharing in cloud computing systems. However, this co-resident VM architecture can be exploited by malicious attackers, posing unique survivability and security risks for cloud users. This paper addresses one of such risks called co-residence attacks, where a malicious attacker can steal or corrupt a user's sensitive information through co-residing the attacker's VM with the target user's VM on the same physical server. We model users' data protection policy in which sensitive data are replicated and stored on different VMs to enhance data survivability. Both user's and attacker's VMs are distributed among cloud servers at random. The arrival of attacker's requests for creating VMs is modeled by a Poisson stochastic process. We propose a probabilistic model to obtain dynamic data survivability and security indices. Based on the suggested evaluation model, dynamic data replication policies are analyzed and optimized. Numerical examples are presented to demonstrate impacts of different model parameters on the dynamic data survivability and security.
A model checking approach for user relationship management in the social network

Purpose The purpose of this paper is to describe how formal verification strategies have been utilized to assess the correctness of Knowledge Creation Process (KCP) in the social systems. This paper analyzes a User Relationship Management (URM) approach in term of human behavior connection in the social systems. A formal framework is displayed for the URM which consolidates behavioral demonstrating strategy. Design/methodology/approach Evaluating the human behavior interactions is an important matter in the social systems. For this analysis, formal verification is an essential section in the complex information systems development. Model checking results satisfied the logical problems in the proposed behavior model analysis. Findings Model checking results represent satisfaction of the logical problems in the proposed behavior model analysis. In the statistical testing, the proposed URM mechanism supported KCP conditions. Also, the percentage of state reachability in the URM with KCP conditions is higher than the URM mechanism without supporting KCP conditions. Originality/value The model checking results show that the proposed URM mechanism with supporting the KCP conditions satisfies comprehensively behavioral interactions rather than the mechanism without KCP conditions in the social networks.
Dynamic Trust Enforcing Pricing Scheme for Sensors-as-a-Service in Sensor-Cloud Infrastructure

In this paper, the problem of provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of competitive sensor-owners, i.e., oligopolistic market, and heterogeneous sensor nodes in service-oriented sensor-cloud is studied. Oligopolistic sensor-owners adopt unfair means to degrade the quality of service provided by other sensor-owners in the sensor-cloud market. In order to address this problem, a dynamic pricing scheme, named DETER, is proposed in this work to enforce trust among the sensor-owners for maintaining the quality of Se-aaS provided by the Sensor-Cloud Service Provider (SCSP). Each sensor node calculates distributed trust opinion for other nodes, while the SCSP calculates centralized trust opinion for each sensor-owner. A Single-Leader-Multiple-Follower Stackelberg Game is formulated in which the SCSP acts as the leader and decides price to be paid to each sensor-owner, while ensuring maximum profit. On the other hand, the sensor-owners act as the followers and decide their strategies for earning maximum profit. Thereby, using DETER, SCSP enforces high trust among the sensor-owners. Additionally, using DETER, energy consumption of sensor nodes in sensor-cloud decreases by 4.69-11.56 percent, and network overhead decreases by 52.6-56.53 percent. The trade-off between price earned by the sensor-owners and profit of the SCSP in service-oriented sensor-cloud is also maintained using DETER.
Content-aware image resizing: An improved and shadow-preserving seam carving method

The performance of seam carving-based image resizing algorithms is strongly dependent on the quality of the importance map extracted from the image. To date, various approaches have been proposed to extract the importance map, however, none has considered to take the shadows within the image into account. In most cases, existing shadows in the images would imply important information and help in better and quick understanding of the content of the images. This fact motivates us to keep them during the image resizing as much as we can. Therefore, in this paper, a seam carving-based algorithm is presented where we introduce an efficient shadow extraction algorithm in the direction of our motivation. We also propose a saliency map for highlighting the salient objects in the images. In addition to these two maps, a gradient map is also extracted to portray the details of the background. These maps are then combined in order to produce the final importance map. Extensive experiments conducted on a large collection of images indicate that the proposed method, in terms of preserving the main content of the input image, the shadows within it, and the important structures of the image, is superior to state-of-the-art algorithms.
First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning

We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek. In contrast to prior work in trajectory forecasting, our algorithm, Darko, goes further to reason about semantic states (will I pick up an object?), and future goal states that are far in terms of both space and time. Darko learns and forecasts from first-person visual observations of the user's daily behaviors via an Online Inverse Reinforcement Learning (IRL) approach. Classical IRL discovers only the rewards in a batch setting, whereas Darko discovers the transitions, rewards, and goals of a user from streaming data. Among other results, we show Darko forecasts goals better than competing methods in both noisy and ideal settings, and our approach is theoretically and empirically no-regret.
Multinational Enterprises and the Provision of Collective Goods in Developing Countries under Formal and Informal Institutional Voids. The Case of Electricity in Sub-Saharan Africa

Despite their unprecedented growth, developing countries still face severe problems in the provision of collective goods. Electricity, whose provision is scarce or unreliable in most developing regions, especially in Sub-Saharan Africa, is an emblematic case. The reason for this shortage is not only imputable to the lack of effective formal institutions, but also to the inefficacy of informal institutions in enabling alternative solutions for the production, transmission and distribution of electricity. We claim that in this context of "double institutional void", foreign direct investment (FDI) and multinational enterprises (MNEs) can play a decisive role. However, their effectiveness depends on both the formal and informal institutional proximity between the home and the host countries. Our empirical analysis relies on panel data models run on a sample of pairs of home-host countries, the latter of which are all from Sub-Saharan Africa (SSA), observed from 2005 to 2011.
Dynamic parameter adaptation in metaheuristics using gradient approximation and line search

Metaheuristics have been established as effective solvers for challenging optimization problems. However, their performance is highly dependent on their parameter settings. For this reason, various parameter tuning techniques have been developed, spanning two major categories: online and offline techniques. Online techniques are based on performance data of the algorithm during its run, while offline techniques are based on preprocessing or historical performance data of the algorithm. Alternatively to these techniques, we propose a general online parameter adaptation method based on estimations of the algorithm’s performance and gradient search in the parameter domain. The proposed method is demonstrated on Differential Evolution, a state-of-the-art metaheuristic for continuous optimization. Our experimental validation includes problems of low and high dimension as well as comparisons with distinguished adaptive algorithms. The obtained results suggest that the proposed approach is beneficial, relieving the user from the burden of proper parameterization.
Exploiting Transaction Accumulation and Double Spends for Topology Inference in Bitcoin

Bitcoin relies on a peer-to-peer network for communication between participants. Knowledge of the network topology is of scientific interest but can also facilitate attacks on the users' anonymity and the system's availability. We present two approaches for inferring the network topology and evaluate them in simulations and in real-world experiments in the Bitcoin testnet. The first approach exploits the accumulation of multiple transactions before their announcement to other peers. Despite the general feasibility of the approach, simulation and experimental results indicate a low inference quality. The second approach exploits the fact that double spending transactions are dropped by clients. Experimental results show that inferring the neighbors of a specific peer is possible with a precision of 71% and a recall of 87% at low cost.
Contextual Speech Recognition with Difficult Negative Training Examples

Improving the representation of contextual information is key to unlocking the potential of end-to-end (E2E) automatic speech recognition (ASR). In this work, we present a novel and simple approach for training an ASR context mechanism with difficult negative examples. The main idea is to focus on proper nouns (e.g., unique entities such as names of people and places) in the reference transcript and use phonetically similar phrases as negative examples, encouraging the neural model to learn more discriminative representations. We apply our approach to an end-to-end contextual ASR model that jointly learns to transcribe and select the correct context items. We show that our proposed method gives up to 53.1% relative improvement in word error rate (WER) across several benchmarks.
Measuring rock microstructure in hyperspectral mineral maps

A novel method is presented to measure rock microstructure in hyperspectral mineral maps of rock specimens. Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks. Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension. The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples. The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is a first step towards quantification of microstructures in mineral maps of rocks. Further development of the methodology could contribute to the creation of unbiased classification scheme of rocks, improved statistical modeling of compositional rock parameters such as mineral ore grades, and the automated recognition of microstructures in large image databases of rocks and drill-core.
Latent Class Modeling of Children’s Preference Profiles on Tangible and Graphical Robot Programming

Contribution: Prior studies on tangible versus graphical user interfaces have reported controversial findings concerning children's preferences. This paper shows that their preference profiles in the domain of introductory programming are associated with gender and age for both interfaces. Background: The relevant literature mainly consists of empirical research into the possible advantages and weaknesses of tangible user interfaces versus graphical user interfaces in the domain of programming for children. Research designs have considered aspects of usability, attractiveness and users' collaboration. Existing findings are not clear. Methodological issues, e.g., limitations of simple statistics, may have limited the clarify and usefulness of findings to date. The present study explores children's preferences profiles. Research Questions: Two research questions were posited: the first focused on the latent profiles of children's preferences for the two programming interfaces (tangible or graphical), and the second focused on the association of these profiles with gender and age. Methodology: Children (N=148) aged from six to 13 years old participated in this paper. Eight variables related to the attractiveness, collaboration, and usability factors for the tangible and graphical programming interfaces were measured and analyzed by Latent Class Analysis, a model-based clustering method that can reveal distinct preference-profiles and associate them with individual differences. Findings: The patterns of responses are consistent across items for both interfaces. The graphical programming interface strongly trisects children's preferences profiles; the three latent profiles encountered were associated with gender and age.
A Scalable Architecture for Uncompressed-Domain Watermarked Videos

Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content.In order to provide every client with a unique, watermarked video, traditional distribution architectures separately encode each watermarked video.However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users.Therefore, this paper proposes a novel architecture that uses fast encoders instead of traditional, full encoders.The fast encoders re-use the coding information from a single, previously-encoded, unwatermarked video in order to speed up the encodings of the watermarked videos.As a result, the complexity of a fast encoder is only a fraction of the complexity of a full encoder.Due to a high correlation of the re-used coding information with the optimal coding information, the compression efficiency and watermark robustness decrease only slightly.Most importantly, the proposed fast encoder speeds up the compression process with a factor of 115, resulting in a low complexity similar to that of a video decoder.Consequently, video distributors can use the proposed architecture to deliver high-quality watermarked videos on a large-scale without requiring an excessive amount of computational resources.
Emotion-Aware Video QoE Assessment Via Transfer Learning

In this paper, we proposed an emotion-aware video quality of experience (QoE) assessment. Specifically, we first detect the user's emotion via transfer learning. Then, we design a personalized video QoE prediction model. Experiments show that compared to the traditional video QoE assessment, emotion-aware video QoE assessment has higher score.
Dual graph regularized compact feature representation for unsupervised feature selection

Abstract As one of the dimensionality reduction methods, unsupervised feature selection aims to select a subset of original features without the labels of data instances. It becomes more and more important since large amounts of unlabelled high dimensional data need to be processed in many machine learning and data mining tasks. In this paper, we propose a novel unsupervised feature selection method via compact feature representation, with the local geometrical structure of data being preserved by using a dual Laplacian graph regularization term. In detail, different to many previous representation based methods which use the original data as representation dictionary, we propose to learn a feature dictionary subspace for compact and robust feature representation. During the dictionary learning process, the l2,1-norm is imposed on the combination coefficient matrix to select discriminate features for constructing the latent feature dictionary. Meanwhile, the local geometrical structure of original data is preserved from the perspectives of subspace learning and feature representation. In general, our method conducts dictionary learning and unsupervised feature selection simultaneously. We develop an efficient optimization algorithm based on Alternating Direction Method of Multipliers to solve the proposed optimization problem and experiments on various types of real world datasets are conducted to demonstrate the effectiveness of the proposed method.
RGB-‘D’ Saliency Detection With Pseudo Depth

Recent studies have shown the effectiveness of using depth information in salient object detection. However, the most commonly seen images so far are still RGB images that do not contain the depth data. Meanwhile, the human brain can extract the geometric model of a scene from an RGB-only image and hence provides a 3D perception of the scene. Inspired by this observation, we propose a new concept named RGB-'D' saliency detection, which derives pseudo depth from the RGB images and then performs 3D saliency detection. The pseudo depth can be utilized as image features, prior knowledge, an additional image channel, or independent depth-induced models to boost the performance of traditional RGB saliency models. As an illustration, we develop a new salient object detection algorithm that uses the pseudo depth to derive a depth-driven background prior and a depth contrast feature. Extensive experiments on several standard databases validate the promising performance of the proposed algorithm. In addition, we also adapt two supervised RGB saliency models to our RGB-'D' saliency framework for performance enhancement. The results further demonstrate the generalization ability of the proposed RGB-'D' saliency framework.