Reviewerly/exHarmony huggingface dataset:
Maximal Uncorrelated Multinomial Logistic Regression

Multinomial logistic regression (MLR) has been widely used in the field of face recognition, text classification, and so on.However, the standard multinomial logistic regression has not yet stressed the problem of data redundancy.That is to say, in multi-class classification, there are many similar features among different classes, which will cause the corresponding classes not being correctly classified.As data redundancy is a common phenomenon in many fields, in response to this phenomenon, this paper proposes a maximal uncorrelated MLR (MUMLR) classification model to solve the problem of data redundancy in multi-class classification.The main idea is to reduce the weight of similar features and try to keep more discriminative information in the data by adding an uncorrelated regularization.In addition, we use the Cauchy-Buniakowsky-Schwarz inequation to scale the original objective function into the convex function and solve it by the Adam optimization method.Its main advantages are as follows: for data with more redundant information, the classification effect of the proposed algorithm is better than the state-of-the-art algorithms.In addition, we prove that the regularization we proposed can also be applied to neural networks and has achieved good results.
Achieving liability in anonymous communication: Auditing and tracing

The emergence of anonymity abusing in anonymous communication has received considerable attention. Achieving the liability of auditing and tracing illegal users becomes to be a critical requirement. Although some anonymity abusing control strategies have been proposed, they mostly possess no prior audit judgment function. In this paper, we propose a linkable group signature mechanism to simultaneously achieve the functions of anonymity, auditing and tracing for communication sender. Specifically, we propose a general construction of linkable group signature using the basic cryptography modules of blind signature, public key encryption, trapdoor indicative commitment and signature of knowledge, and further extend it to a multi-authority environment based on distributed key generation protocol. Following the frameworks, we present concrete linkable group signature instances. Security and performance analyses confirm that our schemes are practical. Furthermore, we first formally define a new concept called trapdoor indicative commitment, which is applicable to judge whether given committed values are equal without opening the commitments.
A statistical evaluation of eye-tracking data of screening mammography: Effects of expertise and experience on image reading

Screening mammography has been widely used over the last few decades to detect breast cancers at an early stage using low-dose X-ray imaging. Being able to detect breast lesions early is highly beneficial for the patients as it makes treatment more successful and increases the chance of recovery. However, lesion detection is prone to false negative and false positive results due to the subtlety or low prevalence of the lesions, which compromises patient safety. It is critical to understand human perception and interpretation of images and use such understanding to develop ways to help increase the accuracy of cancer detection. In this paper, we present a large-scale eye-tracking study where 1,568 eye movement trials of expert breast radiologists, trainee radiologists, and physicists were recorded while assessing a large number of mammogram images. First, we statistically analysed viewers' gaze behaviour based on their mean fixation duration. Results demonstrated that there was no difference amongst experts, and that trainees' mean fixation duration was significantly shorter than experts and physicists' fixation duration was significantly longer than that of the experts. Second, we evaluated spatial gaze deployment of the three viewer groups and quantified the difference between groups. Results showed that trainees and physicists deviated from where expert radiologists looked in images, and trainees performed better than physicists in terms of focusing on regions of interest. Finally, we investigated the saccadic behaviour of viewers and illustrated the differences between viewer groups in terms of saccade amplitudes and orientations.
Enhancing the Reliability of NFV with Heterogeneous Backup

Virtual network function provides tenant with flexible and scalable end-to-end service chaining in the cloud computing and data center environments. However, comparing with traditional hardware network devices, the uncertainty caused by software and virtualization of Network Function Virtualization expands the attack surface, making the network node vulnerable to a certain types of attacks. The existing approaches for solving the problem of reliability are able to reduce the impact of failure of physical devices, but pay little attention to the attack scenario, which could be persistent and covert. In this paper, a heterogeneous backup strategy is brought up, enhancing the intrusion tolerance of NFV SFC by dynamically switching the VNF executor. The validity of the method is verified by simulation and game theory analysis.
Development of Assamese Text-to-speech System using Deep Neural Network

This paper describes the development of a text-to-speech system for Assamese language, using Deep Neural Network (DNN). The system is trained with speech data, collected by a consortium, that is available free of cost for academic use. The DNN based method eliminates the need for a grapheme to phoneme conversion; rather, it synthesizes speech directly from the UTF-8 based Assamese script. The results of objective and subjective evaluations confirm that the Assamese speech synthesized using DNN approach is better than the ones synthesized using the traditional hidden Markov model based text-to-speech system.
Biotite chemistry and the role of halogens in Archaean greenstone hosted gold deposits: A case study from Geita Gold Mine, Tanzania

Geita Hill is one of the largest Tanzanian gold deposits, located southwest of Lake Victoria and ∼2 km along strike from the giant Nyankanga deposit. The deposit is hosted by metasedimentary rocks – dominated by ironstones and intruded by numerous diorite dykes. Textural, structural and geochronological observations indicate that the gold mineralisation overprints all the ductile fabrics and the late orogenic D6 reverse faulting event, suggesting that gold was introduced during later D7 and D8 events. The metamorphic background is characterized by biotite + K-feldspar + actinolite ± pyrhotite, which is overprinted by the gold related alteration consisting of sulfidation fronts, veins and micro-fractures dominated by quartz + biotite + K-feldpar + pyrite. The almost identical mineralogy of the mineralised assemblage and the metamorphic background makes it difficult to identify the source, the effect and the spatial extent of the mineralising fluid. As part of this study detailed analyses of biotite were conducted in order to identify the nature of the mineralising fluid and its spatial effect across the Geita Hill deposit. Results show that the mineralised assemblage is slightly more oxidized (pyrite + magnetite) compared to the metamorphic background (pyrrhotite + magnetite). The intense sulfidation within the ore zone resulted in the formation of Mg-rich biotite, which grades into more Fe-rich biotite away from the ore zone. This change in biotite chemistry causes a shift in the SWIR Fe-OH 2250 nm absorption feature to lower wavelengths, which is also correlated with an increase in gold grade within the mineralised zone. The abundance of F in biotite significantly increases within the ore zone indicating the presence of F-rich hydrothermal fluids during mineralisation. Late timing of mineralisation, synchronous with the emplacement of voluminous high-K granites, and the presence of K- and F-bearing fluids indicate a genetic connection between the mineralising fluids and the late granites. Biotite grains from different deposits types (porphyry, IOCG, lode-Au) appear to have distinct composition suggesting that the source of the fluids involved in the formation of each deposit type has a characteristic halogen chemistry.
LpGL

We present LpGL, an OpenGL API compatible Low-power Graphics Library for energy efficient AR headset applications. We first characterize the power consumption patterns of a state of the art AR headset, Magic Leap One, and empirically show that its internal GPU is the most impactful and controllable energy consumer. Based on the preliminary studies, we design LpGL so that it uses the device's gaze/head orientation information and geometry data to infer user perception information, intercepts application-level graphics API calls, and employs frame rate control, mesh simplification, and culling techniques to enhance energy efficiency of AR headsets without detriment of user experience. Results from a comprehen- sive set of controlled in-lab experiments and an IRB-approved user study with 25 participants show that LpGL reduces up to 22% of total energy usage while adding only 46 sec of latency per object with close to no loss in subjective user experience.
CoPHEE: Co-processor for Partially Homomorphic Encrypted Execution

The recent disclosure of the Spectre and Meltdown side-channel vulnerabilities offers yet another example of modern computer architectures prioritizing performance optimizations over security and privacy. The devastating impact of data leakage, however, emphasizes the need for new processor designs that provide native support for data privacy using cryptography. In this paper, we report on a year-long effort to design, implement, fabricate, and validate CoPHEE: a novel co-processor design that mitigates data leakage risks using partially homomorphic encrypted execution. ASIC designs for encrypted execution impose unique challenges, such as the need for non-traditional arithmetic units (modular inverse, greatest common divisor), very wide datapaths (2048 bits), and the requirement for secure multiplexer units enabling general-purpose execution on encrypted values. Our fully-functional co-processor chip is fabricated in 65nm CMOS technology, and communicates to a main processor via UART. This paper offers an elaborate overview of all steps and design techniques in the ASIC development process, ranging from RTL design to fabrication and validation. We evaluate our co-processor using data-oblivious C++ benchmarks, while our RTL files are available in an open-source repository.
Understanding passwords – a taxonomy of password creation strategies

Purpose Using authentication to secure data and accounts has grown to be a natural part of computing. Even if several authentication methods are in existence, using passwords remains the most common type of authentication. As long and complex passwords are encouraged by research studies and practitioners alike, computer users design passwords using strategies that enable them to remember their passwords. This paper aims to present a taxonomy of those password creation strategies in the form of a model describing various strategies used to create passwords. Design/methodology/approach The study was conducted in a three-step process beginning with a short survey among forensic experts within the Swedish police. The model was then developed by a series of iterative semi-structured interviews with forensic experts. In the third and final step, the model was validated on 5,000 passwords gathered from 50 different password databases that have leaked to the internet. Findings The result of this study is a taxonomy of password creation strategies presented as a model that describes the strategies as properties that a password can hold. Any given password can be classified as holding one or more of the properties outlined in the model. Originality/value On an abstract level, this study provides insight into password creation strategies. As such, the model can be used as a tool for research and education. It can also be used by practitioners in, for instance, penetration testing to map the most used password creation strategies in a domain or by forensic experts when designing dictionary attacks.
Improving Healthcare Processes with Smart Contracts

Currently, we are on the brink of a period of fundamental change for the healthcare expertise of specialists, i.e., existing know-how becomes less available to patients. One of the main reasons is an economic problem: most people and organisations cannot afford the services of first-rate professionals; and most economies are struggling to sustain their professional services, including schools, court systems, and health services. Another reason for the change in healthcare is the rapid growth of evidence-based medical knowledge, where a new research paper is published, on average, every forty-one seconds. Thus, evidence-based medicine is malfunctioning, and delayed, missed, and incorrect diagnoses occur in 10 to 20% of the time. Innovative IT technologies can solve the critical challenges in the healthcare domain. One of such technologies is smart contracts that manage and enforce contracts (business rules) without the interference of a third party. Smart contracts improve interoperability and privacy in cross-organisational processes. In this paper, we define problematic processes in healthcare and then provide a smart contract-based mapping for improving these processes. This paper proposes the way to overcome inequality in services accessibility, inefficient use of services and shortcomings in service quality, using smart contracts and blockchain technology.
Neural adversarial learning for speaker recognition

This paper presents the adversarial learning approaches to deal with various tasks in speaker recognition based on probabilistic discriminant analysis (PLDA) which is seen as a latent variable model for reconstruction of i-vectors. The first task aims to reduce the dimension of i-vectors based on an adversarial manifold learning where the adversarial neural networks of generator and discriminator are merged to preserve neighbor embedding of i-vectors in a low-dimensional space. The generator is trained to fool the discriminator with the generated samples in latent space. A PLDA subspace model is constructed by jointly minimizing a PLDA reconstruction error, a manifold loss for neighbor embedding and an adversarial loss caused by the generator and discriminator. The second task of adversarial learning is developed to tackle the imbalanced data problem. A PLDA based generative adversarial network is trained to generate new i-vectors to balance the size of training utterances across different speakers. An adversarial augmentation learning is proposed for robust speaker recognition. In particular, the minimax optimization is performed to estimate a generator and a discriminator where the class conditional i-vectors produced by generator could not be distinguished from real i-vectors via discriminator. A multiobjective learning is realized for a specialized neural model with the cosine similarity between real and fake i-vectors as well as the regularization for Gaussianity. Experiments are conducted to show the merit of adversarial learning in subspace construction and data augmentation for PLDA-based speaker recognition.
The affordances of art for making technologies

With this inaugural lecture as Socrates Professor on the topic of Making Humane Technologies, I aim to show that artistic practices afford embedding technologies better in society. Analyzing artworks made at RAAAF, an art collective that makes visual art and experimental architecture, I will describe three aspects of making practices that may contribute to improving the embedding of technology in society: (1) the skill of working with layers of meaning; (2) the skill of creating material playgrounds that afford free exploration of the potential of new technologies and artistic experiments; and (3) the skill of openness to the possibility of having radically different socio-material practices. I will use images of several RAAAF projects to make these skills involved in making more tangible. It is artistic skills like these that can contribute to a better societal embedding of technologies.
Substitution box design based on chaotic maps and cuckoo search algorithm

The widespread use of the Internet for transmission of data demands the well-defined data protection algorithms. Symmetric block cipher protects the digital information from the un-authorized recipient. The cryptographic strength of symmetric cipher mainly depends on substitution box (S-box). So the main challenge is to create an S-box with higher value of nonlinearity. Meta-heuristic optimization algorithm has been applied to create a secure S-box. In this paper, a design scheme based on chaotic maps and Cuckoo Search Algorithm (CSA) is proposed to enhance the non-linearity of S-box. Chaotic maps are widely used to create secure S-box due to their aperiodic nature. The proposed idea produced an S-box with non-linearity 109.25, which strengthens the cipher security against linear attacks. The results are compared with the recently created S-boxes and the S-boxes obtained using meta-heuristic algorithms such as Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Firefly algorithm (FA). The obtained S-box is evaluated against cryptographic criteria such as bijectivity, nonlinearity, strict avalanche criteria, bit independence criteria, linear probability and differential probability.
Secure image transmission based on visual cryptography scheme and artificial neural network-particle swarm optimization-guided adaptive vector quantization

Image transmission holds a major share in data communication, and thus secure image transmission is currently a challenging domain of research. A secure image transmission scheme is proposed that physically transmits the encrypted image employing visual cryptography scheme (VCS). During physical transmission, the meaningless shares may attract curious hackers and if captured and stacked, the secret may be revealed. Moreover, the increase in transmission overhead due to multiple share images resulted from a single secret image after encryption is another concern regarding the physical implementation of VCS. Focusing on both observations, vector quantization (VQ) is used to encode as well as to compress each of the shares before transmission. To utilize VQ, its two parameters, cell width and dimension of grid, are needed to be optimized for various kind of images without compromising the randomness property of the shares. Hence, a particle swarm optimization-guided VQ is proposed, and furthermore, a multilayer perceptron in conjunction with an autoencoder are also trained in synchronism with that to automatically obtain the optimal VQ for each image type during the transmission. The proposed scheme is successfully implemented with different types of images for secure physical transmission with a 62.8% data volume reduction and 98.07% image quality retrieval.
Uniform Adaptive Testing Using Maximum Clique Algorithm

Computerized adaptive testing (CAT) presents a tradeoff problem between increasing measurement accuracy and decreasing item exposure in an item pool. To address this difficulty, we propose a new CAT that partitions an item pool to numerous uniform item groups using a maximum clique algorithm and then selects the optimum item with the highest Fischer information from a uniform item group. Numerical experiments underscore the effectiveness of the proposed method.
Sequential and continuous time stick-breaking

The repeated breaking of a linear object, for example a stick, is a fundamental process which underlies numerous natural phenomena.Here we compare two distinct ensembles of stick-breaking: (i) a stick is broken with a certain rate over time; and (ii) a stick is broken a finite number of times.Both ensembles are deduced from appropriate integral equations and related to each other.The analyses performed here and the comparison of the two ensembles enables us to better understand the stick-breaking process by itself.
Collision detection Based on OBB Simplified modeling

Abstract This paper has analysed the most common algorithm of collision detection based on Oriented Bounding Box(OBB) simplified modeling. After that, in order to improve the efficiency of traditional collision detection algorithm in practical applications, an improved OBB collision detection algorithm is proposed.Moreover, a collision detection algorithm and the double robot coordinated motion model were built by matlab software, at the same time, a trajectory where two robots can collide with each other was built. In addition, the two algorithms were compered by changing the calculation accuracy in robot trajectory planning. Finally, the results show that the algorithm can greatly improve the efficiency of collision detection in dual robot systems.
Fast Hue-Division-Based Selective Color Transfer

This paper introduces a new concept, selective color transfer (SCT), to allow for color experimentation and visualization on a selected object within an image, without affecting any other image content or color. To implement this new concept, this paper proposes a new fast SCT-I algorithm for image input data and a new SCT-V algorithm for video input data. For the SCT-I algorithm, its fundamental novelties include: 1) utilizing a new hue-division-based color region segmentation (HCR) based on defining a new nonlinear and circular property of the hue spectrum; 2) developing two new color artifact suppression strategies, including color region integration and circular statistical calculation; and 3) applying a new truncated color transfer equation to solve the problem of mismatches in the data range. The SCT-V algorithm utilizes: 1) object tracking to locate the target of interest (TOI) within each video frame; 2) the SCT-I algorithm to update the original color within TOIs; and 3) an expected color interpolation process to support different special color animation effects. The experimental results demonstrate: 1) there are no residual color artifacts introduced; 2) the approaches have tremendous flexibility for modifying an object's color; and 3) these algorithms have a low computational cost. Furthermore, these algorithms require minimal manual intervention for the easy use of ordinary users. The performance analysis demonstrates the potential of these two methods for various applications, including creating special effects in videos, making histology images much clearer for medical inspection, and enabling low-cost experimentation of manufacturing the design of a product.
Secure 2D Image Steganography Using Recamán's Sequence

This paper presents a double -layer secure message scheme. The first layer is of a cryptographic nature, where the Data Encryption Standard (DES), CAST5 or Blowfish is firstly applied on the message bits for a higher security level. However, the second layer is of steganographic nature, where the encrypted data is hidden in the Least Significant Bits (LSB)s of a 2D cover image using Recamán's Sequence which determines the order of bytes to embed in. The performance of the proposed scheme is measured in terms of the maximum embedding capacity, the mean square error (MSE), the peak signal to noise ratio (PSNR) and the structural similarity index metric (SSIM), as well as histogram analyses, entropy measures and running time complexity.
Support vector machine and regression

This chapter introduces a powerful tool for classification and regression, including linear support vector machines, nonlinear support vector machines, and support vector regression.