Reviewerly/exHarmony huggingface dataset:
Updatable Ciphertext-Policy Attribute-Based Encryption Scheme With Traceability and Revocability

Ciphertext-policy attribute-based encryption (CP-ABE) can offer fine-grained access control over encrypted data, which is suitable for complex commercial applications. However, since the same decryption privileges could be shared by multiple users in the one-to-many encryption mechanism, it is dangerous that a malicious user misuses his secret key but cannot be traced. In addition to further security, when the malicious user has been caught, it is required to revoke him from the system. To address these problems, we propose a novel updatable CP-ABE scheme supporting white-box traceability and traitor revocation. In the proposed scheme, a “fixed point” is embedded into the user's secret key to achieving the traceability and each user is assigned with a unique identifier for revocation. Moreover, the secret exponent used to encrypt a message is divided into two parts: one is assigned to access policy and the other to the revocation list. Therefore, only a part of the ciphertext components needs to be updated when the revocation list is changed, which greatly simplifies the process of ciphertext update. Compared to the previous works, our scheme is more efficient, and can achieve valid revocation and ciphertext update. In addition, the traceability of the proposed scheme is depended on the l-Strong Difffie-Hellman assumption, and the indistinguishability security under selective access policy and chosen-plaintext attacks in the standard model is reduced to the Decisional q-Bilinear Diffie-Hellman assumption. Furthermore, the experimental results show that the proposed scheme is efficient.
Software project scheduling problem in the context of search-based software engineering: A systematic review

This work provides a systematic literature review of the software project scheduling problem, in the context of search-based software engineering, and summarizes the main models, techniques, search algorithms and evaluation criteria applied to solve this problem. We also discuss trends and research opportunities. Our keyword search found 438 papers, published in the last 20 years. After considering the inclusion and exclusion criteria and performing the snowballing procedure, we have analyzed 37 primary studies. The results show the predominance of the use of evolutionary algorithms. The static model, in which the scheduling is performed once during the project, is considered in the majority of the papers. Synthetic instances are commonly used to validate the heuristic and hypervolume and execution time are the mostly applied evaluating criteria.
Clemmys

We introduce Clemmys, a security-first serverless platform that ensures confidentiality and integrity of users' functions and data as they are processed on untrusted cloud premises, while keeping the cost of protection low. We provide a design for hardening FaaS platforms with Intel SGX---a hardware-based shielded execution technology. We explain the protocol that our system uses to ensure confidentiality and integrity of data, and integrity of function chains. To overcome performance and latency issues that are inherent in SGX applications, we apply several SGX-specific optimizations to the runtime system: we use SGXv2 to speed up the enclave startup and perform batch EPC augmentation. To evaluate our approach, we implement our design over Apache Open-Whisk, a popular serverless platform. Lastly, we show that Clemmys achieved same throughput and similar latency as native Apache OpenWhisk, while allowing it to withstand several new attack vectors.
Individual Differences in Embodied Distance Estimation in Virtual Reality

There are important individual differences when experiencing VR setups. We ran a study with 20 participants who got a scale-matched avatar and were asked to blind-walk to a VR target placed 2.5 meters away. In such setups, people typically underestimate distances by approximately 10% when virtual environments are viewed through head mounted displays. Consistent with previous studies we found that the underestimation was significantly reduced the more embodied the participants were. However, not all participants developed the same level of embodiment when exposed to the exact same conditions.
An Efficient Low-Latency Point-Multiplication Over Curve25519

The elliptic curve cryptography (ECC) has gained attention mainly due to its lower complexity compared to other asymmetric methods while providing the same security level. The most performance critical operation in ECC is the point multiplication. Thus, its efficient implementation is desirable. One of the most secure and lightweight ECC curves, which satisfies all standard security criteria, is the Curve25519. In this paper, a low latency Karatsuba-Ofman-based field multiplier (KOM) and an efficient point multiplication over Curve25519 have been proposed. The improvements have been achieved mainly due to the proposed low latency pipelined KOM and efficient scheduling of field operations. Moreover, an enhanced architecture exploiting a number of well-known differential power analysis (DPA) countermeasures have been implemented. To evaluate this work against the previous ones, FPGA has been exploited to implement the point multiplication architectures. The FPGA implementation results of the enhanced and regular architectures show at least 64% and 33% improvement in A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> T (slice_count × time) compared to those in the previous work, respectively. However, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> T (DSP_count × time) metrics in both architectures seem to be similar to the previous work. Furthermore, the time complexity of both enhanced and regular architectures decreases 89% using Zynq-7020.
Cocks–Pinch curves of embedding degrees five to eight and optimal ate pairing computation

Recent algorithmic improvements of discrete logarithm computation in special extension fields threaten the security of pairing-friendly curves used in practice. A possible answer to this delicate situation is to propose alternative curves that are immune to these attacks, without compromising the efficiency of the pairing computation too much. We follow this direction, and focus on embedding degrees 5 to 8; we extend the Cocks–Pinch algorithm to obtain pairing-friendly curves with an efficient ate pairing. We carefully select our curve parameters so as to thwart possible attacks by “special” or “tower” Number Field Sieve algorithms. We target a 128-bit security level, and back this security claim by time estimates for the DLP computation. We also compare the efficiency of the optimal ate pairing computation on these curves to $$k=12$$ curves (Barreto–Naehrig, Barreto–Lynn–Scott), $$k=16$$ curves (Kachisa–Schaefer–Scott) and $$k=1$$ curves (Chatterjee–Menezes–Rodríguez-Henríquez).
Benchmarking Deep Learning Frameworks and Investigating FPGA Deployment for Traffic Sign Classification and Detection

We benchmark several widely-used deep learning frameworks and investigate the field programmable gate array (FPGA) deployment for performing traffic sign classification and detection. We evaluate the training speed and inference accuracy of these frameworks on the graphics processing unit (GPU) by training FPGA-deployment-suitable models with various input sizes on German Traffic Sign Recognition Benchmark (GTSRB), a traffic sign classification dataset. Then, selected trained classification models and various object detection models that we train on GTSRB's detection counterpart (i.e., German Traffic Sign Detection Benchmark) are evaluated with inference speed, accuracy, and FPGA power efficiency by varying different parameters such as floating-point precisions, batch sizes, etc. We discover that Neon and MXNet deliver the best training speed and classification accuracy on the GPU in general for all test cases, while TensorFlow is always among the frameworks with the highest inference accuracies. We observe that with the current OpenVINO release, the performance of lightweight models (e.g., MobileNet-v1-SSD, etc.) usually exceeds the requirement of real-time detection without losing much accuracy, while other models (e.g., VGG-SSD, ResNet50-SSD) generally fail to do so. We also demonstrate that we can adjust the precision of bitstreams and the batch sizes to balance inference speed and accuracy of the applications deployed on the FPGA. Finally, we show that for all test cases, the FPGA always achieves higher power efficiency than the GPU.
Normalized Neural Networks for Breast Cancer Classification

In almost all parts of the world, breast cancer is one of the major causes of death among women. But at the same time, it is one of the most curable cancers if it is diagnosed at early stage. This paper tries to find a model that diagnose and classify breast cancer with high accuracy and help to both patients and doctors in the future. Here we develop a model using Normalized Multi Layer Perceptron Neural Network to classify breast cancer with high accuracy. The results achieved is very good (accuracy is 99.27%). It is very promising result compared to previous researches where Artificial Neural Networks were used. As benchmark test, Breast Cancer Wisconsin (Original) was used.
A Socially Assistive Robot for Elderly Exercise Promotion

The population ageing phenomenon leads to an unceasing need for home-based healthcare systems to continuously monitor the elderly's cognitive and physical health.In this sense, physical activity may be beneficial in preserving cognition in elder life as well as in providing clinicians and therapists with the indicative of elderly's health condition.Nevertheless, current systems fail to promote and monitor the elderly's physical activity in their living environments.This paper is aimed at providing a socially assistive robot solution for this task.Since robot acceptance depends to a great extent on its robustness in performing tasks, we have focused on exercise recognition due to its great importance for both clinicians and elderly.For that, two different tasks were carried out.First, an image dataset for physical exercise recognition has been generated.Then, a comparative analysis of several deep learning techniques is presented.This paper reveals a great performance in the exercise recognition of CNN-LSTM with an exercise recognition accuracy of 99.87%.
A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors vulnerable to a range of adversarial attacks. Thus, this paper surveys the attacks against Twitter spam detectors in an adversarial environment, and a general taxonomy of potential adversarial attacks is presented using common frameworks from the literature. Examples of adversarial activities on Twitter that were discovered after observing Arabic trending hashtags are discussed in detail. A new type of spam tweet (adversarial spam tweet), which can be used to undermine a deployed classifier, is examined. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors to such attacks are investigated.
A network-based concept extraction for managing customer requests in a social media care context

Web 2.0 changed everyday life in many aspects, including the whole system that orbits around the purchase of products and services. This revolution necessarily involved also companies, because customers became increasingly demanding. The diffusion of social media platforms pushed customers to prefer this channel for quickly obtaining information and feedback about what they want to buy, as well as for asking help after the selling. In this framework, many organisations adopted a new way of providing assistance known as social customer care. A direct link to companies allows customers to obtain real-time solutions. In this paper, we introduce a new strategy for automatically managing the information listed in the requests that customers send to the social media accounts of companies. Our proposal relies on the use of network techniques for extracting high-level structures from texts, highlighting the different concepts expressed into the customers’ written requests. The texts can be then organised on the basis of this new emerging information. An application to the requests sent to the AppleSupport service on Twitter shows the effectiveness of the strategy.
Spatial Context Energy Curve-Based Multilevel 3-D Otsu Algorithm for Image Segmentation

While yielding satisfactory segmentation results for images with low SNR and poor contrast, one-dimensional (1-D) and two-dimensional (2-D) Otsu's thresholding methods have the downside of high computational complexity. So far, three-dimensional (3-D) Otsu method has been based on histogram, which has only probability distribution of pixels as an object of interest. Histogram-based segmentation methods do not consider the contextual information which is significant to enrich the quality of segmented image. In this paper, a context-based 3-D Otsu algorithm has been proposed that considers the pixel intensity values as well as spatial information along with same properties of histogram. The proposed method is evaluated comprehensively with respect to quality and a detailed analysis is presented to compare the results of histogram-based 1-D, 2-D, and 3-D Otsu and energy-based 1-D, 2-D, and 3-D Otsu method, respectively. Experimental outcomes demonstrate the superiority of energy-based 3-D Otsu algorithm compared to histogram-based methods in terms of improved performance metrics, including mean error (ME), mean square error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), and entropy. Experiments on standard daily life color images have been carried out to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.
BlockchainDB - Towards a Shared Database on Blockchains

In this demo we present BlockchainDB, which leverages blockchains as storage layer and introduces a database layer on top that extends blockchains by classical data management techniques (e.g., sharding). Further, BlockchainDB provides a standardized key/value-based query interface to facilitate the adoption of blockchains for data sharing use cases. With BlockchainDB we can thus not only improve the performance and scalability of blockchains for data sharing but also decrease the complexity for organizations intending to use blockchains for this use case.
Breast Cancer Diagnosis Using Image Processing and Machine Learning for Elastography Images

As a trending medical imaging technique, Elastography and B-mode (ultrasound) are combined as a diagnostic tool to differentiate between benign and malignant breast lesions based on their stiffness and geometric properties. Image processing techniques are applied to the resulting images for feature extraction. Data preprocessing methods and principal component analysis (PCA) as a dimensionality reduction technique are applied to the dataset. In this paper, supervised learning algorithm “support vector machine (SVM)” is used for the classification of combined elastogram and B-mode images. Model validation is performed with K-fold cross-validation to ensure the generalization of the algorithm. Accuracy, confusion matrix, and logistic loss are then evaluated for the used algorithm. The maximum classification accuracy is 94.12% when using SVM with radial basis function (RBF) kernel.
Gender classification on 2D human skeleton

Soft bimetrics has become a trending research topic over the past decade. In last years, the increase of new technologies such as the wearable camera devices has introduced a new challenge into the gender classification problem. In this sense, the ability to classify the gender not by an image but by the 2D estimated skeleton points is considered in this paper. Our experiments show that the human gender can be classified just considering the pose information provided by the body pose information. The proposed method have shown a remarkable performance on a dataset where subjects and camera are in movement.
Urdu Named Entity Recognition

Named Entity Recognition (NER) plays a pivotal role in various natural language processing tasks, such as machine translation and automatic question-answering systems. Recognizing the importance of NER, a plethora of NER techniques for Western and Asian languages have been developed. However, despite having over 490 million Urdu language speakers worldwide, NER resources for Urdu are either non-existent or inadequate. To fill this gap, this article makes four key contributions. First, we have developed the largest Urdu NER corpus, which contains 926,776 tokens and 99,718 carefully annotated NEs. The developed corpus has at least doubled the number of manually tagged NEs as compared to any of the existing Urdu NER corpora. Second, we have generated six new word embeddings using three different techniques, fastText, Word2vec, and Glove, on two corpora of Urdu text. These are the only publicly available embeddings for the Urdu language, besides the recently released Urdu word embeddings by Facebook. Third, we have pioneered in the application of deep learning techniques, NN and RNN, for Urdu named entity recognition. Finally, we have performed 10-folds of 32 different experiments using the combinations of a traditional supervised learning and deep learning techniques, seven types of word embeddings, and two different Urdu NER datasets. Based on the analysis of the results, several valuable insights are provided about the effectiveness of deep learning techniques, the impact of word embeddings, and variations of datasets.
GAugur

Cloud gaming has been very popular recently, but providing satisfactory gaming experiences to players at a modest cost is still challenging. Colocating several games onto one server could improve server utilization. To enable efficient colocations while providing Quality of Service (QoS) guarantees, a precise quantification of performance interference among colocated games is required. However, achieving such precise interference prediction is very challenging for games due to the complexity introduced by the contention on many shared resources across CPU and GPU. Moreover, the distinctive properties of cloud gaming require that the prediction model should be constructed beforehand and the prediction should be made instantaneously at request arrivals, which further increases the difficulty. The existing solutions are either not applicable or not effective due to many limitations. In this paper, we present GAugur, a novel methodology that enables highly accurate prediction of the performance interference among games arbitrarily colocated. By leveraging machine learning technologies, GAugur is able to capture the complex relationship between the interference and the contention features of colocated games. We evaluate GAugur through extensive experiments using a large number of real popular games. The results show that GAugur is able to identify whether a colocated game satisfies QoS requirement within an average error of 5%, and is able to quantify the performance degradation of a colocated game within an average error of 7.9%, which significantly outperforms the alternatives. Moreover, GAugur incurs an offline profiling cost linear to the number of games, and negligible overhead for online prediction. We apply GAugur to guiding efficient game colocations for cloud gaming. Experimental results show that GAugur is able to increase the resource utilization by 20% to 60%, and improve the overall performance by up to 15%, compared to the state-of-the-art solutions.
A linearly convergent doubly stochastic Gauss–Seidel algorithm for solving linear equations and a certain class of over-parameterized optimization problems

Consider the classical problem of solving a general linear system of equations $$Ax=b$$ . It is well known that the (successively over relaxed) Gauss–Seidel scheme and many of its variants may not converge when A is neither diagonally dominant nor symmetric positive definite. Can we have a linearly convergent G–S type algorithm that works for anyA? In this paper we answer this question affirmatively by proposing a doubly stochastic G–S algorithm that is provably linearly convergent (in the mean square error sense) for any feasible linear system of equations. The key in the algorithm design is to introduce a nonuniform double stochastic scheme for picking the equation and the variable in each update step as well as a stepsize rule. These techniques also generalize to certain iterative alternating projection algorithms for solving the linear feasibility problem $$A x\le b$$ with an arbitrary A, as well as high-dimensional minimization problems for training over-parameterized models in machine learning. Our results demonstrate that a carefully designed randomization scheme can make an otherwise divergent G–S algorithm converge.
Organizational practices as antecedents of the information security management performance

Purpose The purpose of this paper is to expand current knowledge about the security organizational practices and analyze its effects on the information security management performance. Design/methodology/approach Based on the literature review, the authors propose a research model together with hypotheses. The survey questionnaires were developed to collect data, which then validated the measurement model. The authors collected 111 responses from CEOs at manufacturing small- and medium-sized enterprises (SMEs) that had already implemented security policies. The hypothesized relationships were tested using the structural equation model approach with EQS 6.1 software. Findings Results validate that information security knowledge sharing, information security education and information security visibility, as well as security organizational practices, have a positive effect on the information security management performance. Research limitations/implications The consideration of organizational aspects of information security should be taken into account by academics, practitioners and policymakers in SMEs. Besides, the work helps validate novel constructs used in recent research (information security knowledge sharing and information security visibility). Practical implications The authors extend previous works by analyzing how security organizational practices affect the performance of information security. The results suggest that an improved performance of information security in the industrial SMEs requires innovative practices to foster knowledge sharing among employees. Originality/value The literature recognizes the need to develop empirical research on information security focused on SMEs. Besides the need to identify organizational practices that improve information security, this paper empirically investigates SMEs’ organizational practices in the security of information and analyzes its effects on the performance of information security.