Reviewerly/exHarmony huggingface dataset:
Local Deep Features for Composite Face Sketch Recognition

Face sketch recognition is an important task for forensic investigations. In the last decades most of the sketches used by law enforcement agencies are composites made from real photos, using some specialized software. In this work we propose a new method for the automatic recognition of these composite sketches. We propose to use deep learning features extracted from facial components to represent the sketches. We decide to use intermediate layers from already trained deep models and we employ a metric learning approach, as an easier way to learn the differences between sketch and photo domains. The experimental evaluation conducted on two available databases, shows the superiority of the proposal with respect to the use of the original deep models, as well as to other state-of-the-art methods.
A Bayesian Game Theoretic Approach for Inspecting Web-Based Malvertising

Web-based advertising systems have been exploited by cybercriminals to disseminate malware to an enormous number of end-users and their vulnerable machines. To protect their malicious ads and malware from detection by the ad network, malvertisers apply various redirection and evasion techniques. Meanwhile, the ad network can also apply inspection techniques to spoil the malvertiser's tricks and expose the malware. However, both the malvertiser and the ad network are under resource and time constraints. Moreover, the ad network is disadvantaged because it has incomplete information about whether it is facing a benign or malicious advertiser. In this paper, we aim to apply the Bayesian game model by designing two games to formulate the problem of inspecting the Web-based maladvertising. The first game has two types of Advertisers, namely Malicious and Benign, and one type of Defender; the second game has two types of Attackers, Advanced and Simple, in terms of their capability of redirection and evasion, and one type of Defender. We define their strategies and payoff functions, and compute their Bayesian Nash equilibria. We use numeric simulation to evaluate our game theoretic models, and we derive several insights from the results that can serve as guidelines for the ad network to decide its best inspection strategy.
Entropy squeezing for three-level atom interacting with a single-mode field*

The entropy squeezing for a three-level atom interacting with a single-model field is studied. A general definition of entropy squeezing for three-level atom is given according to entropic uncertainty relation of three-level system, and the calculation formalism of entropy is derived for a cascade three-level atom. By using numerical calculation, the entropy squeezing properties of a cascade three-level atom are examined. Our results show that, three-level atom can generate obvious entropy squeezing effect via choosing appropriate superposition state of three-level atom. Our results are meaningful for preparing three-level system information resources with ultra-low quantum noise.
A multi-objective interval valued fuzzy clustering algorithm with spatial information for noisy image segmentation

Interval valued fuzzy c-means (IVFCM) clustering algorithm is one of effective clustering algorithms. When applied to image segmentation, IVFCM includes three problems as follows: (1) It is sensitive to the initial values of algorithm and may easily fall into the local optimal. (2) The algorithm is sensitive to the image noise and cannot obtain the satisfying performance on images corrupted by noise. (3) It always performs image segmentation under one objective function, therefore it cannot meet multiple practical needs. In order to address these problems, a multi-objective interval valued fuzzy clustering algorithm is proposed in this paper. This method constructs two novel interval valued fuzzy fitness functions which utilize the non-local spatial information of the image. Then a new mutation operator combining the interval valued fuzzy information of image is designed. Furthermore, an effective interval valued fuzzy cluster validity index using the non-local spatial information of image is presented to select a single solution from the non-dominated solution set. Experimental results show that the proposed method behaves well in noisy image segmentation.
Opinion Spam Detection through User Behavioral Graph Partitioning Approach

Online reviews, an important source of user opinions, help not only other customers to make a decision but also manufacturers to improve quality of their products or services. Due to commercial reasons, untruthful reviews (spam) written to promote or demote certain products rather than they deserve have become a crucial problem. Although existing supervised approaches have shown the effectiveness of spam detection by using statistical learning, they require much expensive cost for labeling the training data. In this paper, we present BeGP that is a graph-partitioned approach for opinion spam detection. A set of characteristic features is first extracted and a user behavioral graph is constructed by connecting reviewers sharing those features to capture their similar behavior. BeGP is a semi-supervised scheme without requiring any training. Hence, it starts with a small subgraph of labeled spammers and afterwards iteratively expands by conducting connected other users as a resulted set of suspects. We demonstrate the effectiveness of BeGP on two real-world review datasets from Yelp.com. The result shows that it outperforms several state-of-the-art methods with accurately identifying spammers as well as review spams within the k-first order of ranking.
Convolutional neural networks for recognition and segmentation of aluminum profiles

Learning to automatically recognize objects in the real world is a very important and stimulating challenge. This work deals with the problem of detecting aluminium profiles within images, using hierarchical representations such as those based on deep learning methods. The use of regional CNN, a conceptually simple, flexible, and general framework for object instance segmentation, allows to exceed the previous state-of-the-art results. This approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. Neural network training uses ResNet networks of depth 50 or 101 layers. In particular, the training dataset consists of synthetic data generated by CAD files. The Dataset creation process is fundamental: experimental results show that trivial datasets lead to poor detection performance. A rich dataset, instead, including more complex images, allows the network to learn more and better guaranteeing excellent results. How to get more data, if you do not have more data? To get more data, we just need to make minor alterations such as flips, scale or rotations to existing dataset. This process is known as Data augmentation. The performance of the proposed system strongly depends on the dataset used for training and on the backbone architecture used. This why, adopting a strategy that generates a priori a very large dataset the time required to create the annotation file grows almost exponentially. The validation and test dataset, on the other hand, consists of real images captured by cameras. The distinctiveness of this work consists to train a deep neural network with synthetic data (aluminium CAD files) and verify if it on real data. Experiments show that the implementation of architecture, as described above, leads to good performance in automatic detection and classification. Future work will be addressed to improve the network training process together with the architecture, the algorithms and the dataset creation process. The latter is proved to be fundamental for the balance and optimization of the whole process. The way is to develop not much augmented datasets focusing on online data augmentation during network training.
Uniquely determined pure quantum states need not be unique ground states of quasi-local Hamiltonians

We consider the problem of characterizing states of a multipartite quantum system from restricted, quasi-local information, with emphasis on uniquely determined pure states. By leveraging tools from dissipative quantum control theory, we show how the search for states consistent with an assigned list of reduced density matrices may be restricted to a proper subspace, which is determined solely by their supports. The existence of a quasi-local observable which attains its unique minimum over such a subspace further provides a sufficient criterion for a pure state to be uniquely determined by its reduced states. While the condition that a pure state is uniquely determined is necessary for it to arise as a non-degenerate ground state of a quasi-local Hamiltonian, we prove the opposite implication to be false in general, by exhibiting an explicit analytic counterexample. We show how the problem of determining whether a quasi-local parent Hamiltonian admitting a given pure state as its unique ground state is dual, in the sense of semidefinite programming, to the one of determining whether such a state is uniquely determined by the quasi-local information. Failure of this dual program to attain its optimal value is what prevents these two classes of states to coincide.
Simple shear response of a thin sheet of polytetrafluoroethylene under large deformations

Rigid-body motions and deformations of a thin sheet of polytetrafluoroethylene (PTFE) under simple shear at large deformations were experimentally investigated. The Modified slotted shear test was performed under quasi-static conditions at constant temperature. A new approach was developed to improve the accuracy of measurements in five sub-regions along shear path of each specimen. The proposed approach consisted of three main steps: estimation of displacement fields, determination and removal of translational and rotational motions and evaluation of pure deformations. Displacement fields were obtained using the Digital Image Correlation (DIC) method. Amount of shear, normal extensions, principal stretches and principal directions were evaluated from pure deformations using the displacement fields. Results showed that all sub-regions did not exhibit considerable rotations for values of amount of shear up to 0.4. A contraction of the analyzed regions at the same direction of the applied loads was observed, while the normal extension remained almost unchanged in the direction perpendicular to the applied loads. For sub-regions near notch tips at large values of amount of shear, angle of rotation of 5° and 10% of contraction were observed. Overall, the influence of rigid-body motions and normal extensions on the principal stretches and principal directions was relatively small, mainly in the middle of the specimen. Moreover, a simple constitutive model was used to describe the shear response of PTFE.
User Interfaces for Counteracting Decision Manipulation in Group Recommender Systems

In group recommender systems,decision manipulation refers to an attack in which a group member makes attempts to push his/her favorite options. In this paper, we propose user interfaces to counteract decision manipulation in group recommender systems. The proposed user interfaces visualize information dimensions regarding rating adaptations of group members at different transparency levels. The results show that the user interface at the highest transparency level best helps to discourage users from decision manipulation. Besides, the ability of the user interfaces to counteract decision manipulation differs depending on the dimensions represented in the user interfaces. The information dimensions regarding "\textititem ratings " and "\textitgroup recommendations " have the strongest impacts on preventing users from decision manipulation.
Predictive Prototyping for Real-World Applications

In this paper we explore the fusion of a sophisticated user interface prototyping tool with a cognitive modeling approach based on the ACT-R cognitive architecture. The resulting tool allows the creation of complex interactive prototypes for which quantitative performance predictions can be derived by running cognitive models. Such models are automatically generated by monitoring a designer's interaction while completing a task scenario with a prototype. Using the example of a real-world user interface for a software-controlled riveting machine, we demonstrate the scope of the approach and compare model predictions to data gathered in two empirical studies. The encouraging goodness-of-fit of the predictions strongly suggests that the use of such simulated interactions with prototypes provides a promising and cost-effective approach to predict learning trajectories and performance data for routine tasks. We argue that predictive prototyping fosters the acceleration of iteration cycles without resulting in additional expenditures for UX designers.
Natural Language Processing and Classification Methods for the Maintenance and Optimization of US Weapon Systems

The Logistics Management Institute (LMI) works with the US Department of Defense (DoD) in analyzing maintenance logs on US weapons systems. A major issue in processing this data is determining how to extract useful information from disorganized short-form texts in order to optimize the maintenance of these systems. Unlike text from other corpora, these text entries are only a few words in length and do not conform to lexical convention. LMI has provided a subset of about 10 million of these maintenance logs, each labeled with action-object pairs. The goals of this research are to construct a model that predicts action-object pairs and provide a metric to assess its validity. Prior to analysis, the entries are vectorized by either TFIDF and TSVD, or Word2vec. Several models are applied, including logistic regression, k-NN, SVM, decision trees, LSA, and DBSCAN clustering. Unsupervised models are tested in addition to supervised models due to the ambiguity regarding the validity of the provided ground truth values. The results of these tests yield accuracy scores of about 0.53 for action words and 0.73 for object words. Furthermore, the results from clustering provides evidence for discrepancies in the ground truth values. Taking this into consideration, prior models are adjusted and accuracy scores increased to 0.78 for action words.
A dynamic wrapper‐based feature selection for improved precision in content‐based image retrieval

Summary The content‐based image retrieval (CBIR) has been applied in the image processing as well as pattern recognition. A challenging task in the CBIR research has been through the feature extraction for decreasing any semantic gap that is an active research topic. Here, in this work, there is a texture feature that is extracted from an image, making use of a technique of curvelet transform. This curvelet is selected for a sparse representation that is quite critical for the estimation of images, which have been de‐noised with some inversed problems. The wrapping‐based curvelet transform will be even more robust as well as faster in the time of computation than the Ridge Transform. A technique of feature selection will be brought for selecting the optimal features. The correlation‐based feature selection (CFS) method has been adapted for improving the accuracy of the CBIR systems. The non‐deterministic polynomial (NP)–hard problems may be solved using the chemical reaction optimization (CRO) that has been motivated using the technique of chemical reaction. The benefits of proposed method is a necessity for marginal human intrusion for the purpose of retrieving the images needed from its database. The proposed mechanism has been again assessed depending on the Coral Database, and a performance study is ended using precision, f measure, and recall.
Can Personas Speak? Biopolitics in Design Processes

In this provocation, we problematize the use of personas as an interaction design method. We explore whether the use of personas within design processes prevents meaningful participation, with reference to research with third-sector organizations in the United Kingdom. Personas should help designers and developers to empathize with the people they are designing for, but we find that their use is biopolitical, turning singular experiences into static, reusable design resources. We call for a research agenda focused on the ways design processes prevent participation, encode power relations, and entrench marginalization.
Investigating fast re-identification for multi-camera indoor person tracking

In person tracking applications involving multiple cameras, person re-identification is an important step for ensuring accurate tracking of individuals as they move between camera views. However, changes in camera parameters and environmental conditions can make re-identification challenging. This is especially difficult in resource-constrained environments, as is often the case in many real-world intelligent applications. In this paper, we explore dimensionality reduction, metric learning, and classification for achieving re-identification in a computationally efficient way. We report that the covariance metric transformation is a sufficient distance metric for achieving good linear separability between identity classes, and produces better results than more complex approaches across two re-identification datasets. We also explore one-shot learning methods for performing classification, show that our Sequential k-Means algorithm outperforms other fast one-shot learning approaches, and discuss parameter tuning to improve accuracy.
Dynamic prediction of interface level using spatial temporal markov random field

Maintaining a desired interface level plays a key role for the efficient extraction in oil sands, mining and related industries. However, varying throughputs and downstream disturbances tend to change the interface level over time. Hence, measurement of the interface level is an important indicator of the process behavior and is useful for economical operation and improved control. In this paper, we propose a Spatial Temporal Markov Random Field (ST-MRF) based image segmentation approach for the dynamic prediction of the interface level. We assume that interface level, which is modeled as MRF, propagates as a Markov chain across time, yielding a ST-MRF model. The proposed approach is validated using the images captured from laboratory scale equipment designed to simulate the industrial scenarios.
Two-in-one oblivious signatures

Edge computing is a kind of distributed computing as opposed to the computation in a centralized cloud environment. Oblivious signatures provide similar concept which allow a signature requester and one or multiple signer(s) to cooperatively generate a valid signature. It is a kind of digital signature with privacy protection which was first introduced by Chen in 1994. Later in 2008, Tso et. al. formally defined its security model. Previous schemes provide two different constructions for two different functionalities (ie., signer-oblivious and message-oblivious). In this paper, we firstly introduced the two-in-one oblivious signature which combines the two constructions of oblivious signatures into one scheme. In our scheme, a signature requester can ask many (say n1) possible signers to sign many (say n2) possible messages. At the end of the protocol, our scheme guarantees that only 1 out of the n1 signers has signed 1 out of the n2 messages. In addition, during the signing process, no one (including the possible signers) knows who has really signed the message as well as which one of the n2 message has been signed. Our scheme provides signer-oblivious and message-oblivious simultaneously. Oblivious signature is useful in many edge-computing applications when authentication is required. For example, e-shopping, e-lottery and e-auction etc. We give two examples on how to construct such an oblivious signature based on the hardness of discrete logarithm assumption. The first one is based on the Schnorr signature and the other one is based on a variant type of ElGamal signature. We will give a formal model on the two-in-one oblivious signature scheme and prove the security in the random oracle model.
Design research in education. A practical guide for early career researchers

As befitting a treatise on design research, this volume steers a course between the pragmatics of formulating and conducting design research, on the one hand, and on the other, the ensemble of theo...
Hardware Acceleration of SHA-256 Algorithm Using NIOS-II Processor

Cryptography plays a leading role in the information transmission security. Even though encrypted, data can be altered when transmitted over a network. Therefore, to verify data the use of a digital signature is essential. Hashing algorithms are used both to create digital signatures and to verify the receiving data. Our proposed design of a Hashing algorithm, is a custom hardware implementation on a FPGA of the Secure Hash Algorithm-2 (SHA-2), using Nios II processor. We use two acceleration techniques, to improve performance of SHA-2 computation: floating point and floating point 2. Finally, after applying these techniques, we studied all the designs in terms of integration time, the number of clock cycles and the percentage of overlap in the FPGA.
The Effect of Physics-Based Corrections and Data Augmentation on Transfer Learning for Segmentation of Benthic Imagery

Ocean observation has been greatly improved by the use of Autonomous Underwater Vehicles and Remotely Operated Vehicles, and the high quality and high quantity of imagery they produce. This quantity of images collected on research cruises has, however, become intractable using traditional manual image analysis methods. There is a growing need for automation of this process, using methods such as deep learning to analyse and summarise the information present, yet research into how to improve their performance for underwater images is currently limited. This paper presents a study into the effect of using physics-based corrections and data augmentation to aid the performance of DeepLabV3, a state of the art image segmentation system. Using metadata about the physical environment the images were taken in, particularly the altitude of the vehicle for each image, and the known wavelength dependent light attenuation over distance through water, reduces the generalisation error of the DeepLabV3 system.
A Gesture Interaction System Based on Improved Finger Feature and WE-KNN

In most gesture recognition research fields, feature extraction is based on single finger. In this paper, we propose an improved finger feature extraction algorithm based on double fingers, and it is easier to judge the projected distance and angle of five fingertips and can be better applied to gesture recognition with multi-fingers' information. Furthermore, K-nearest neighbor (KNN) classifier based on entropy-weight allocation (WE-KNN) is proposed to improve the accuracy of gesture recognition. Compared with traditional algorithms, the proposed finger feature extraction algorithm combined with WE-KNN can enhance the accuracy of gesture recognition on Leap Motion dataset. What's more, we apply the proposed algorithms to the gesture interaction system of virtual gym scenario, which can increase the interactive fun and improve the interactive experience.
Simplex regression models with measurement error

This paper considers the simplex regression model when there is measurement error in the covariate. We consider a structural approach where the measurement error follows a normal or gamma distribution. We apply a Monte Carlo EM algorithm to estimate the parameters using a pseudo-likelihood function. A simulation study is used to investigate the impact of ignoring the measurement error. Finally, the results are illustrated with a data set.