Reviewerly/exHarmony huggingface dataset:
Mimicry Attacks on Smartphone Keystroke Authentication

Keystroke behaviour-based authentication employs the unique typing behaviour of users to authenticate them. Recent such proposals for virtual keyboards on smartphones employ diverse temporal, contact, and spatial features to achieve over 95% accuracy. Consequently, they have been suggested as a second line of defense with text-based password authentication. We show that a state-of-the-art keystroke behaviour-based authentication scheme is highly vulnerable against mimicry attacks. While previous research used training interfaces to attack physical keyboards, we show that this approach has limited effectiveness against virtual keyboards. This is mainly due to the large number of diverse features that the attacker needs to mimic for virtual keyboards. We address this challenge by developing an augmented reality-based app that resides on the attacker’s smartphone and leverages computer vision and keystroke data to provide real-time guidance during password entry on the victim’s phone. In addition, we propose an audiovisual attack in which the attacker overlays transparent film printed with spatial pointers on the victim’s device and uses audio cues to match the temporal behaviour of the victim. Both attacks require neither tampering or installing software on the victim’s device nor specialized hardware. We conduct experiments with 30 users to mount over 400 mimicry attacks. We show that our methods enable an attacker to mimic keystroke behaviour on virtual keyboards with little effort. We also demonstrate the extensibility of our augmented reality-based technique by successfully mounting mimicry attacks on a swiping behaviour-based continuous authentication system.
Capabilities of Chatbots and Its Performance Enhancements in Machine Learning

To keep in pace with ever-increasing customer demands, providing instant and useful responses is a prominent need of service providers. Latest technical developments have led to the advent of a faster, easier solution: chatbot. It is an artificial intelligence-based simulation of human conversation to automate customer interactions, thereby reducing manual effort. Not necessarily limited to answering simple product-related queries, chatbots can provide complex predictive analysis within limited response time, by the help of machine learning. Creating a chatbot has become simplistic enough, even for any non-technical person. It can be configured and integrated into any messenger within an organization or social network. This paper discusses the capabilities of chatbots and the enhancements in their performance by the contribution of machine learning.
A Flexible Processing-in-Memory Accelerator for Dynamic Channel-Adaptive Deep Neural Networks

With the success of deep neural networks (DNN), many recent works have been focusing on developing hardware accelerator for power and resource-limited embedded system via model compression techniques, such as quantization, pruning, low-rank approximation, etc. However, almost all existing DNN structure is fixed after deployment, which lacks runtime adaptive DNN structure to adapt to its dynamic hardware resource, power budget, throughput requirement, as well as dynamic workload. Correspondingly, there is no runtime adaptive hardware platform to support dynamic DNN structure. To address this problem, we first propose a dynamic channel-adaptive deep neural network (CA-DNN) which can adjust the involved convolution channel (i.e. model size, computing load) at run-time (i.e. at inference stage without retraining) to dynamically trade off between power, speed, computing load and accuracy. Further, we utilize knowledge distillation method to optimize the model and quantize the model to 8-bits and 16-bits, respectively, for hardware friendly mapping. We test the proposed model on CIFAR-10 and ImageNet dataset by using ResNet. Comparing with the same model size of individual model, our CA-DNN achieves better accuracy. Moreover, as far as we know, we are the first to propose a Processing-in-Memory accelerator for such adaptive neural networks structure based on Spin Orbit Torque Magnetic Random Access Memory(SOT-MRAM) computational adaptive sub-arrays. Then, we comprehensively analyze the trade-off of the model with different channel-width between the accuracy and the hardware parameters, eg., energy, memory, and area overhead.
CMOS Annealing Machine: A Domain-Specific Architecture for Combinatorial Optimization Problem

Domain-specific architectures are being studied to improve computer performance beyond the end of Moore's Law. Here, we propose a new computing architecture, the CMOS annealing machine, which provides a fast means of solving combinatorial optimization problems. Our architecture is based on in-memory computing architecture through utilizing the locality of interactions in the Ising model. The prototype presented in 2019 has two processors on a business-card-sized board and solves problems 55 times faster than conventional computers.
Deep Learning in the Healthcare Industry

Artificial Neural networks (ANN) are composed of nodes that are joint to each other through weighted connections. Deep learning, as an extension of ANN, is a neural network model, but composed of different categories of layers: input layer, hidden layers, and output layers. Input data is fed into the first (input) layer. But the main process of the neural network models is done within the hidden layers, ranging from a single hidden layer to multiple ones. Depending on the type of model, the structure of the hidden layers is different. Depending on the type of input data, different models are applied. For example, for image data, convolutional neural networks are the most appropriate. On the other hand, for text or sequential and time series data, recurrent neural networks or long short-term memory models are the better choices. This chapter summarizes the state-of-the-art deep learning methods applied to the healthcare industry.
Privacy‐preserving data aggregation scheme for edge computing supported vehicular ad hoc networks

Abstract Vehicular ad hoc networks (VANETs) allow vehicles getting road situation by connecting with roadside unit (RSU) through the on‐board unit, and this information can be processed to improve transportation services in a timely manner. Meanwhile, the three‐tier edge computing system can support the VANETs well. However, the limited bandwidth of the VANETs and the privacy of uploaded data are still issues to be resolved. Data aggregation is an effective way to address the issue of limited bandwidth and data privacy protection for individual devices. In this article, based on the bilinear pairing and Paillier homomorphic encryption, we design a privacy‐preserving data aggregation scheme for edge computing supported VANETs, which not only preserves the privacy of uploaded data but also realizes batch operations. Also, the proposed scheme guarantees the features of data integrity and source authentication. Moreover, the comparison of performance evaluation between ours and other recent schemes shows that the proposed scheme significantly reduces the communication overhead and is more suitable for resource‐constrained terminal device.
Refutation Systems: An Overview and Some Applications to Philosophical Logics

Refutation systems are systems of formal, syntactic derivations, designed to derive the non-valid formulas or logical consequences of a given logic. Here we provide an overview with comprehensive references on the historical development of the theory of refutation systems and discuss some of their applications to philosophical logics.
Effective GasPrice Prediction for Carrying Out Economical Ethereum Transaction

In Ethereum, reaching a transaction consensus costs a certain number of gases, which should be purchased by users in their self-defined gas prices. Generally, the higher the gas price, the shorter the time is spent on reaching consensus. Since the transaction gas prices still vary greatly in a block, generating a reasonable price that can make a trade-off between the consensus time and the gases cost is of great significance. In this paper, we propose a Machine Learning Regression-based gas price predicting approach (MLR), aiming to find the lowest transaction gas price in the next block for carrying out economical Ethereum transaction. Specifically, we identify five influencing factors (i.e., difficulty, block gas limit, transaction gas limit, ether price, and miner reward) from the Ethereum transacting process and resort the classic machine learning regression to build the predicting model. Our empirical study on 194,331 blocks implies that the proposed MLR approach works well and can save $17,552.2 for all transactions in the 74.9% accuracy.
Hand Posture Recognition Using Skeletal Data and Distance Descriptor

In this paper, a method for the recognition of static hand postures based on skeletal data was presented. A novel descriptor was proposed. It encodes information about distances between particular hand points. Five different classifiers were tested, including four common methods and a proposed modification of nearest neighbor classifier, which can distinguish between posture classes differing mostly in hand orientation. The experiments were performed using three challenging datasets of gestures from Polish and American Sign Languages. The proposed method was compared with other approaches found in the literature. It outperforms every compared method, including our previous work, in terms of recognition rate.
Disease Classification Using Linguistic Neuro-Fuzzy Model

In recent years, due to advancement in medical technologies and its devices, a large volume of medical data is generated continuously from different sources at every moment. Analyzing these large volumes of medical data and correctly diagnosing the diseases are challenging tasks. Generally, these medical data contain uncertain, imprecise, and incomplete information that affects the performance of the classification model. In this paper, Linguistic Neuro-Fuzzy (LNF) model is used for the classification of diseases. First, this model uses a linguistic fuzzification process that computes the membership values of each feature to overcome the uncertainty issues. Second, these membership values are passed to the ANN-based model to predict the disease. The objective of this research work lies in the applications of this LNF model that predicts the diseases. The effectiveness of this model is tested and validated using six benchmark medical datasets. The performance of the LNF model is compared with ANN and observed that the LNF model outperforms than ANN to handle the uncertainty problem.
Deep compositional robotic planners that follow natural language commands

We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and manipulate objects. Our approach combines a deep network structured according to the parse of a complex command that includes objects, verbs, spatial relations, and attributes, with a sampling-based planner, RRT. A recurrent hierarchical deep network controls how the planner explores the environment, determines when a planned path is likely to achieve a goal, and estimates the confidence of each move to trade off exploitation and exploration between the network and the planner. Planners are designed to have near-optimal behavior when information about the task is missing, while networks learn to exploit observations which are available from the environment, making the two naturally complementary. Combining the two enables generalization to new maps, new kinds of obstacles, and more complex sentences that do not occur in the training set. Little data is required to train the model despite it jointly acquiring a CNN that extracts features from the environment as it learns the meanings of words. The model provides a level of interpretability through the use of attention maps allowing users to see its reasoning steps despite being an end-to-end model. This end-to-end model allows robots to learn to follow natural language commands in challenging continuous environments.
Automatic Language Identification Using Speech Rhythm Features for Multi-Lingual Speech Recognition

The conventional speech recognition systems can handle the input speech of a specific single language. To realize multi-lingual speech recognition, a language should be firstly identified from input speech. This study proposes an efficient Language IDentification (LID) approach for the multi-lingual system. The standard LID tasks depend on common acoustic features used in speech recognition. However, the features may convey insufficient language-specific information, as they aim to discriminate the general tendency of phonemic information. This study investigates another type of feature characterizing language-specific properties, considering computation complexity. We focus on speech rhythm features providing the prosodic characteristics of speech signals. The rhythm features represent the tendency of consonants and vowels of languages, and therefore, classifying them from speech signals is necessary. For the rapid classification, we employ Gaussian Mixture Model (GMM)-based learning in which two GMMs corresponding to consonants and vowels are firstly trained and used for classifying them. By using the classification results, we estimate the tendency of two phonemic groups such as the duration of consonantal and vocalic intervals and calculate rhythm metrics called R-vector. In experiments on several speech corpora, the automatically extracted R-vector provided similar language tendencies to the conventional studies on linguistics. In addition, the proposed R-vector-based LID approach demonstrated superior or comparable LID performance to the conventional approaches in spite of low computation complexity.
Social media and inclusive human development in Africa

This study investigates the relationship between social media and inclusive human development in 49 African countries for the year 2012. Social media is measured with Facebook penetration whereas inclusive human development is proxied by the inequality- adjusted human development index. The empirical evidence is based on Ordinary Least Squares, Tobit and Quantile regressions. Ordinary Least Squares provided baseline results, Tobit regressions account for the limited range in the outcome variable while Quantile regressions are engaged to control for initial levels of inequality-adjusted human development. From Ordinary Least Squares and Tobit results, Facebook penetration is positively associated with inclusive human development. Quantile regressions confirm this positive nexus and further establish that the positive association is slightly higher in magnitude in the above-median sub-sample. From a comparative assessment, it is apparent that with the exception of the resource-wealth sub-samples, higher levels of Facebook penetration are associated with comparatively higher levels of inclusive human development. Accordingly, the positive association between Facebook penetration and inclusive human development is: (i) a positive function of income levels and (ii) more apparent in Middle East and North African countries (compared to Sub-Saharan African countries), English common law countries (compared to their French civil law counterparts), and coastal countries (in relation to landlocked countries).
Towards Prioritizing GitHub Issues

The vast growth in usage of GitHub by developers to host their projects has led to extensive forking and open source contributions. These contributions occur in the form of issues that report bugs or pull requests to either fix bugs or add new features to the project. On the other hand, massive increase in the number of issues reported by developers and users is a major challenge for integrators, as the number of concurrent issues to be handled is much higher than the number of core contributors. While there exists prior work on prioritizing pull requests, in this paper we make an attempt towards prioritizing issues using machine learning techniques. We present the Issue Prioritizer, a tool to prioritize issues based on three criteria: issue lifetime, issue hotness and category of the issue. We see this work as an initial step towards supporting developers to handle large volumes of issues in projects.
Complexity Evaluation of Constructing Method for Saturated Best Resilient Functions in Stream Cipher Design

The role of the nonlinear Boolean function in the design of secure stream cipher considered as an important approach in the design of Stream cipher cryptosystem. It is well known that the main aspect in the stream cipher security is the Key generators, which generate a key sequence, which will be used to encrypt the data. This paper introduces a method for constructing the Saturated Best Resilient Functions (SBRS), and provides an evaluation to the nonlinearity, resiliency, algebraic degree and complexity of the SBRS functions, that designed to enhance the security of stream cipher crypto-systems.
Training Task Allocation in Federated Edge Learning: A Matching-Theoretic Approach

Federated edge learning has emerged as a promising technique to enable distributed machine learning using local datasets from large-scale edge devices, e.g., mobile phones or parked vehicles, that share only model updates without uploading raw training data. This technique not only preserves data privacy of edge devices but also simultaneously ensures high learning performance. However, the emerging federated edge learning still confronts serious challenges, such as the lack of efficient training task assignment schemes with reliable edge devices acting as workers. To address this challenge, we utilize a many-to-one matching model to solve the training task assignment problem between the workers and multiple task publishers. In the matching model, we minimize not only the overall training time of the task publishers but also the energy consumption of the workers. To define against malicious model updates from unreliable workers, we present reputation as a metric to evaluate the reliability and trustworthiness of the edge devices, and also take the reputation into consideration when assigning training tasks. The numerical results indicate that the proposed schemes can efficiently improve the performance of federated edge learning.
Semi-supervised network embedding with text information

Network embedding plays a pivotal role in network analysis, due to the capability of encoding each node to a low-dimensional dense feature vector. However, most existing network embedding approaches only focus on preserving structural information in the network. The text features and category attributes of nodes are ignored, which are important to network analysis. In this paper, we propose an innovative semi-supervised network embedding (SNE) model integrating structural information, text features and category attributes into embedding vectors simultaneously. Specifically, we design a structure preserving module and a text representation module to capture the global structural information and the text features separately. Meanwhile, a label indicator matrix and a supervised loss are proposed for preserving category information and mapping nodes in the same class closer. We utilize stacked auto-encoders to explore the highly nonlinear characteristics of the network. By optimizing the reconstruction loss and the designed supervised loss jointly in the proposed semi-supervised model, the embedding vectors are finally learned. Extensive experiments on real-world datasets demonstrate that our method is superior to the state-of-the-art baselines in a variety of tasks, including visualization, node classification and clustering.
Parallel execution combinatorics with metaheuristics: Comparative study

Optimization arises everywhere in industrial and engineering fields, with complex and time-consuming problems to be solved. Exact search techniques cannot afford practical solutions for most of the real-life problems in reasonable time-bound. Metaheuristics proved to be numerically efficient solvers for such problems in terms of solution quality, however, they could require large time and energy to get the optimal solution. Parallelization (i.e., distributed) is a promising approach for overcoming the overwhelming energy and time consumption values of these methods. Despite recent approaches in running metaheuristics in parallel, the community still lacks for novel studies comparing and benchmarking the canonical optimization techniques while being running in parallel. In this work, we present two extensive studies to the solution quality, energy consumption, and execution time for three different metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their distributed counterparts. The main aim of our studies is exploring the efficiency of parallel execution of the metaheuristics while being running in new computing environments. Here, we want to identify the combinatorics between metaheuristics and solving optimization problems while being run in parallel. For our studies, we consider a multicore machine with 32 cores. This choice to a recent and commonly used system shall enrich the existing literature for multicore systems against the enormous existing studies over cluster systems. The analyses and discussions for the results of the different algorithms exhibit the combinatorics between the different metaheuristics and the parallel execution using a different number of cores. The outcome of these studies builds a guide for future designs of efficient and energy-aware optimization techniques.
PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups

Support vector machines are supervised learning models which are capable of classifying data and measuring regression by means of a learning algorithm. If data are linearly separable, a conventional linear kernel is used to classify them. Otherwise, the data are normally first transformed from input space to feature space, and then they are classified. However, carrying out this transformation is not always practical, and the process itself increases the cost of training and prediction. To address these problems, this paper puts forward an SVM kernel, called polygonal fuzzy weighted or PFW, which effectively classifies data without space transformation, even if the groups in question are not linearly separable and have overlapping areas. This kernel is based on Gaussian data distribution, standard deviation, the three-sigma rule and a polygonal fuzzy membership function. A comparison of our PFW, radial basis function (RBF) and conventional linear kernels in identical experimental conditions shows that PFW produces a minimum of 26% higher classification accuracy compared with the linear kernel, and it outperforms the RBF kernel in two-thirds of class labels, by a minimum of 3%. Moreover, Since PFW runs within the original feature space, it involves no additional computational cost.