Reviewerly/exHarmony huggingface dataset:
dK-Personalization: Publishing Network Statistics with Personalized Differential Privacy

Preserving privacy of an individual in network structured data while enhancing utility of published data is one of the most challenging problems in data privacy. Moreover, different individuals might have different privacy levels based on their own preferences, thereby personalization needs to be considered to achieve personal data protection. In this paper, we aim to develop a privacy-preserving mechanism to publish network statistics, particularly degree distribution, and joint degree distribution, which guarantees personalized (edge or node) differential privacy while enhancing network data utility. To this extend we propose four approaches to handle personal privacy requirements of individuals in a differentially private computation. We have empirically verified the utility enhancement and privacy guarantee of our proposed approaches on four real-world network datasets. To the best of our knowledge, this is the first study to publish network data distributions under personalized differential privacy, while enhancing network data utility.
Writer identification using VLAD Encoding of the Histogram of Gradient Angle Distribution

The use of computers and automatic systems has enabled scientific researchers to improve the classification rate in the field of writer identification. In our paper, we will propose an identification system based on the use of Histogram of Gradient Angle Distribution (HGAD) in square patches centered around Harris Keypoint locations. A global descriptor per image is calculated subsequently via the VLAD encoding of the local descriptors relating to the histograms of the square patches. The study carried out on two public datasets CVL and BFL made it possible to achieve very interesting identification rates with 99.4% in BFL and 99.7% in CVL.
Pattern Recognition in Convolutional Neural Network (CNN)

One of the main topics of human science research is to explain the mechanism of brain activity and the essence of human intelligence, create intelligent machines similar to human intelligent activities, and develop the technology and intelligent application of human intelligence. The research on pattern recognition using CNN technology is a hot topic. Therefore, this paper studies the problems faced by pattern recognition and explores it. Firstly, this paper discusses the research background and significance of CNN, and briefly introduces the characteristics of CNN. Then it summarizes the relevant articles at home and abroad. Then it briefly describes the BP neural model used in eep and its existing reasons, and explains the better effects they may bring. Finally, a series of experimental tests are carried out on pattern recognition. It is concluded that the network with scaling parameter set to 4 and depth of about 26 has the best effect, which can effectively solve the problem of pattern recognition.
Text Based Emotion Detection by Using Classification and Regression Model

In South Asia Urdu is widely spoken language. Limited amount of literature for Urdu language is exit but more is required for better implementation of NLP tasks. In text-based representation the emotions play vital role. Emotions are expression to express the feeling about any condition, but this expression is not easily understandable in text. In this research the major focus is to identify the emotions from roman Urdu. In the literature emotion-based work is reported on several different languages but limited literature is found for specifically Roman Urdu. It is needed to explore this area because Roman Urdu has also significant importance like other languages. In emotion detection the text is used instead of facial expression which quite different. Aim of study is to summarize each sentence in a single emotion tag. Each tag is a annotation for the expression which give a independent sense. Ten-fold cross validation is performed. Emotion analyses have many useful applications like product feedback, improvement in product quality and mental health. To address the issue of emotional polarity of Roman Urdu sentence, corpora of 5K is developed from different domain and annotate four emotion classes. For the validation and verification of this work few baseline algorithms are used on our corpus. An experiment is conducted by using KNN, DT, SVM and RF algorithm and better F measure score is achieved.
Gaussian sum state estimators for three dimensional angles-only underwater target tracking problems

Gaussian sum filters are considered to be more accurate in terms of estimation accuracy when compared to the conventional algorithms. In this work, Gaussian sum state estimation algorithms are implemented for three dimensional angles-only target tracking problem. Shifted Rayleigh filter (SRF) has been considered as the most accurate estimation algorithms for bearings-only tracking, with moderate computational load. Therefore, SRF formulated in the Gaussian sum framework is developed for solving three dimensional angles-only target tracking problems. The estimation accuracy of the developed algorithm, and other Gaussian as well as Gaussian sum algorithms is validated in terms of percentage track-loss and root mean square error (RMSE).
Is On-Line Handwriting Gender-Sensitive? What Tells us a Combination of Statistical and Machine Learning Approaches

Handwriting is an everyday life human activity. It can be collected off-line by scanning sheets of paper. The resulting images can then be processed by a computer-based system. Thanks to digitizing tablets, handwriting can also be collected on-line. From the collected raw signals (pen position, pressure over time), the dynamics of the writing can be recovered. Since handwriting is unique for each individual, it can be considered as a biometric modality. Biometric systems predicting gender from off-line handwriting, have thus been recently proposed. However we observe that, in contrast to other modalities such as speech, it is not straightforward for a human being (even expert) to predict gender. In this study we explore the limits of automatic gender prediction from on-line handwriting collected from a young adults population, homogeneous in terms of age and education. Statistical analysis of on-line dynamic features can highlight differences between male and female groups [6]. In the present study, we focus on a sentence copying task, and provide statistically significant features to a classifier, based on a machine learning approach (SVMs). Since the dataset is relatively small (240 subjects), several evaluation frameworks are explored: cross validation (CV), bootstrap, and fixed train/test partitions. Accuracies obtained from fixed partitions range from 37% to 79%, while those estimated by CV and bootstrap are around 65%. This shows to our opinion the limits of the gender recognition task for our young adult population dataset.
Variable-Bit-Rate Video Frame-Size Prediction by the Extended Kalman Filter Using Levenberg–Marquardt Algorithm

It is crucial to dynamically predict the future frame-sizes (bit-rates) for multimedia networking. All of the conventional bit-rate predictors are based on the assumption that instantaneous bit-rates are known precisely all the time (in the absence of uncertainty) which is surely not realistic in practice. In this work, we propose a new expectation-maximization (EM) based extended Kalman filter (EKF) to predict the bit-rates, where the EKF state-transition models will be optimized by the Levenberg–Marquardt algorithm (LMA). The main advantages of our proposed novel EKF-based bit-rate prediction approach are given as follows. First, our proposed EKF-based predictor can optimally estimate the bit-rates in the presence of uncertainty and/or noise. Second, our proposed novel EKF-based bit-rate prediction approach does not require a separate classifier to determine the individual frame-types as the conventional approach so our approach would be more robust than the conventional approach. Numerical evaluation of bit-rate (frame-size) prediction is also conducted over three movies encoded by the MPEG-4 standard. Compared to the existing Kalman-filter based bit-rate prediction methods, our proposed new LMA-EKF predictor can achieve much better performance in terms of the normalized mean square error (NMSE) and the inverse of signal-to-noise-ratio (SNR).
A Nature-Inspired Metaheuristic Optimization Algorithm Based on Crocodiles Hunting Search (CHS)

The increasing difficulty of actual-world optimization problems has prompted computer researchers to regularly produce additional process improvement techniques. Metaheuristic and evolutionary computing are very popular in nature-inspired optimization methods. This paper introduces the crocodile search algorithm (CHS), which is a revision of a new metaphorical algorithm based on the hunting behavior of crocodile herds. Various adaptive and arbitrary variables are combined within this algorithm to indicate the exploitation and investigation of the exploration area in various discoveries of optimization. The performance of the CHS is measured in different test phases. Initially, a collection of famous experiment events including unimodal, multi-modal, and composite functions are applied to examine exploitation, exploration, local optima avoidance, and convergence of CHS. The CHS algorithm achieves a regular frame for the airfoil with a pretty low drag, which explains that the methods can be efficient while working physical difficulties including restrained plus unknown search.
Analysis of the Impact of Blockchain and Internet of Things (BIoT) on Public Procurement

Most countries in the world are currently faced with a series of public procurement challenges. Moreover, the large volumes of public procurement and the impact it may have of the global economy, the environment and the society at large justify a research study aimed at achieving sustainable and smart procurement. Smart public procurement intensively relies on emerging technologies and it is both an international priority and a challenge to achieve it. This paper is aimed at addressing such procurement-specific challenges. This study reflects the current status and the trends in public procurement, as well as the manner in which Blockchain and the Internet of Things (BIoT) may lead to a beneficial change in the field. In order to analyse the impact of BIoT, we are putting forward an assessment model comprising the definition and the description of six hypotheses. They are validated both by reference to the current knowledge status and via the analysis of the data collected in a survey which was conducted in Romania. It was aimed at collecting and analysing the data from the main stakeholders as well as at formulating recommendations/actions related to the modernisation of the current system. The study uses structural equation modelling (SEM) to validate the proposed model and to establish the relationships between the adoption of BIoT and smart, sustainable and transparent public procurement. At the same time, we analyse the links between the adoption of BIoT and aspects such as corruption and fraud, the challenges related to technological integration and the need to reengineer organisations, as well as national and international policies. Following our analyses, there emerged that BIoT adoption has a positive impact on the achievement of sustainable public procurement processes (the highest effect), on transparency and the trust in public procurement, on reducing corruption and fraud in public procurement and on the achievement of smart public procurement. The paper provides theoretical and practical contributions that should support solutions to the current major challenges and represent a vehicle for innovation and sustainable development alike.
A Survey on Delegated Computation

In the area of delegated computation, the main problem asks how a computationally weaker client device can obtain help from one or more computationally stronger server devices to perform some computation. Desirable guarantees for the client include correctness of the computation and privacy of the inputs, regardless of any server malicious behavior. In this survey, we review techniques in the area of single-server delegated computation, focusing on a representative subset of algebraic operations that are components of many cryptographic schemes: (a) ring multiplication, (b) cyclic group exponentiation, and (c) bilinear-map pairing. We describe and analyze examples of delegation protocols for these operations, discuss the state of the art, report numeric performance results, and point at several directions for future research.
Ensembling and Score-Based Filtering in Sentence Alignment for Automatic Simplification of German Texts

Among the well-known accessibility services for audiovisual media are subtitling for the deaf and hard-of-hearing, audio description, and sign language interpreting. More recently, automatic text simplification has emerged as a topic in the context of media accessibility, with research often approaching the task as a case of (sentence-based) monolingual machine translation. This approach relies on large amounts of high-quality parallel data, which is why monolingual sentence alignment has gained momentum. Alignment for text simplification is a complex task, with alignments often taking the form of n:m (in contrast to the standard case of 1:1 in machine translation). In this contribution, we evaluate the performance of different alignment methods against a human-created gold standard of standard German/simplified German sentence alignments created from a number of parallel corpora. Two of the corpora contain multiple levels of simplification. We employ a variety of alignment methods developed for monolingual tasks and bilingual sentence alignment. We explore strategies such as ensembling and score-based filtering to further improve the performance over these baselines. We show that combining multiple alignment methods with various hard voting strategies can outperform even the best individual methods and that we achieve similar results with score-based filtering of extracted alignments to find the most promising candidates. Our results motivate the notion that the overall task of sentence alignment for automatic simplification of German should be viewed as a two-step process that goes beyond the application of individual alignment methods.
Sliced Sparse Gradient Induced Multi-View Subspace Clustering via Tensorial Arctangent Rank Minimization

Multi-view clustering method tries to improve the performance of clustering by using the information existing in different views. The tensorial representation is more suitable to capture the high order correlations across different views while keep local geometrical structure in specific view. In this paper, we propose a sliced sparse gradient induced multi-view subspace clustering method via tensorial arctangent rank minimization, named SSG-TAR method. Firstly, a tensorial arctangent rank (TAR) is defined, which is a tighter surrogate of the tensor rank and more effective to explore the consistency among multiple views. Secondly, a sliced sparse gradient regularization (SSG) is firstly proposed to enhance the discrimination between clusters and better capture the complementary information in view-specific feature space. Finally, we unify these two terms together and establish an efficient algorithm to optimize the proposed model. Furthermore, the constructed sequence was proved to converge to the stationary KKT point. We have carried out extensive experiments on ten datasets across different types and sizes to verify the performance of our model. The experimental results show that our method have achieved the state-of-the-art performance.
Verified Password Generation from Password Composition Policies

Password managers (PMs) are important tools that enable the use of stronger passwords, freeing users from the cognitive burden of remembering them. Despite this, there are still many users who do not fully trust PMs. In this paper, we focus on a feature that most PMs offer that might impact the user’s trust, which is the process of generating a random password. We present three of the most commonly used algorithms and we propose a solution for a formally verified reference implementation of a password generation algorithm. We use EasyCrypt to specify and verify our reference implementation. In addition, we present a proof-of-concept prototype that extends Bitwarden to only generate compliant passwords, solving a frequent users’ frustration with PMs. This demonstrates that our formally verified component can be integrated into an existing (and widely used) PM.
Open Challenges in Federated Machine Learning

Federated machine learning is an innovative technique to allow one to train machine learning models mainly on distributed (user) devices not to share private data with third parties. Each device contributes by training a partial model based on local data; a centralized orchestrator aggregates the results. Federated machine learning enhances data privacy and reduces network overhead, but it requires complex coordination mechanisms that handle many devices connected to a potentially unstable network. While this approach is gaining more and more traction, this article tries to summarize and highlight the key challenges that are still open and calls for further contributions from both academia and industry.
Accelerating Simulated Quantum Annealing with GPU and Tensor Cores

Inspired by quantum annealing, simulated quantum annealing (SQA) mimics quantum tunneling effects on classical computers to perform annealing through a path-integral Monte Carlo simulation, which increases the potential to find the global optima faster than traditional annealing algorithms for large-size combinatorial optimization problems while today’s quantum annealing systems are of a limited number of qubits’. As previous studies have accelerated SQA with Graphics Processing Unit (GPU) and specialized hardware such as Field Programmable Gate Array (FPGA), we propose an innovative parallelizing strategy called hierarchical update to vastly improve the efficiency of parallel computing, which is capable of accelerating state-of-the-art SQA implementations further by 7X-47.2X based on our case studies. Furthermore, we develop a tensorizing scheme to leverage the Tensor Cores on modern GPUs to deliver up to 1.83X of additional speedup. Overall, our work solves fully-connected Ising models faster than any previous SQA work. Our solution outperforms existing GPU-based solutions by 86.6X and FPGA-based solutions by 14X.
Point-Mass Filter: Density Specific Grid Design and Implementation

The paper deals with the state estimation of nonlinear stochastic dynamic systems. The stress is laid on a grid-based numerical solution to the Bayesian recursive relations using the point-mass method. In particular, two topics are discussed: first, a novel design of unequally located grid points resulting in improved estimation accuracy with lower computational complexity, second, efficient implementation in the publicly available MATLAB toolbox Nonlinear Estimation Framework. The proposed and implemented point-mass filter is then validated by numerical simulations.
Reliability of 5G in human health monitoring using blockchain technology

The fifth generation wireless networks are going to dominate the present mode of transmission. The advancements in the technology provides a better support by interfacing the gadgets with the network. In addition, 5G enjoys more benefits in data integrity, security, decentralization, and information interchange between different network vendors. In 5G networks, a huge number of Internet of Things (IoT) gadgets are associated. With higher rates, these devices are empowered and fast processing is possible. Blockchain is a significant innovation that is utilized in medical services such as disease prediction and smart treatment. Broadening this blockchain technology to the IoT can have more benefits. The significant issue in 5G innovation is security because a large number of IoT devices are associated and more private information is forwarded. This information ought to be safer utilizing blockchain. This proposed framework is to get information in a more reliable manner utilizing blockchain in 5G networks to keep the information safe and avoid data fraud by maintaining strong privacy.
Patch Tracking-Based Streaming Tensor Ring Completion for Visual Data Recovery

Tensor completion aims to recover the missing entries of a partially observed tensor by exploiting its low-rank structure, and has been applied to visual data recovery. In applications where the data arrives sequentially such as streaming video completion, the missing entries of the tensor need to be dynamically recovered in a streaming fashion. Traditional streaming tensor completion algorithms treat the entire visual data as a tensor, which may not work satisfactorily when there is a big change in the tensor subspace along the temporal dimension, such as due to strong motion across the video frames. In this paper, we develop a novel patch tracking-based streaming tensor ring completion framework for visual data recovery. Given a newly incoming frame, small patches are tracked from the previous frame. Meanwhile, for each tracked patch, a patch tensor is constructed by stacking similar patches from the new frame. Patch tensors are then completed using a streaming tensor ring completion algorithm, and the incoming frame is recovered using the completed patch tensors. We propose a new patch tracking strategy that can accurately and efficiently track the patches with missing data. Further, a new streaming tensor ring completion algorithm is proposed which can efficiently and accurately update the latent core tensors and complete the missing entries of the patch tensors. Extensive experimental results demonstrate the superior performance of the proposed algorithms compared with both batch and streaming state-of-the-art tensor completion methods.
Towards a Systematic Method for Developing Meta Attack Language Instances

Successfully developing domain-specific languages (DSLs) demands language engineers to consider their organizational context, which is challenging. Action design research (ADR) provides a conceptual framework to address this challenge. Since ADR's application to the engineering of DSLs has not yet been examined, we investigate applying it to the development of threat modeling DSLs based on the Meta Attack Language (MAL), a metamodeling language for the specification of domain-specific threat modeling languages. To this end, we conducted a survey with experienced MAL developers on their development activities. We extract guidelines and align these, together with established DSL design guidelines, to the conceptual model of ADR. The research presented, aims to be the first step to investigate whether ADR can be used to systematically engineer DSLs.