Reviewerly/exHarmony huggingface dataset:
Superstring-Based Sequence Obfuscation to Thwart Pattern Matching Attacks

User privacy can be compromised by matching user data traces to records of their previous behavior. The matching of the statistical characteristics of traces to prior user behavior has been widely studied. However, an adversary can also identify a user deterministically by searching data traces for a pattern that is unique to that user. Our goal is to thwart such an adversary by applying small artificial distortions to data traces such that each potentially identifying pattern is shared by a large number of users. Importantly, in contrast to statistical approaches, we develop data-independent algorithms that require no assumptions on the model by which the traces are generated. By relating the problem to a set of combinatorial questions on sequence construction, we are able to provide provable guarantees for our proposed constructions. We also introduce data-dependent approaches for the same problem. The proposed obfuscation methods are evaluated on synthetic data traces and on the Reality Mining Data set to demonstrate the performance of the proposed algorithms relative to alternatives.
An Efficient im2row-based Fast Convolution Algorithm for ARM Cortex-M MCUs

With the rise of IoT and edge computing, deploying neural networks (NNs) on low-power edge computing devices is drawing more and more attention.In NNs, convolutional layers take up the majority of the computing cycles, especially when NNs are implemented on ARM processors [1].Therefore, it is necessary to optimize the convolutional implementation on ARM Cortex-M MCUs.This paper proposes an efficient im2row-based fast convolution algorithm with two innovations.First, a novel im2row method for reusing the data of adjacent convolutional windows is presented.This method utilizes a reusable im2row buffer for data reuse, significantly reducing the amount of data copied during im2row and improving efficiency.Second, in algorithm implementation, a q7_t to q15_t data type extension technique that avoids data reordering is employed.This technique eliminates data reordering instructions, thus reducing the runtime of the algorithm.We evaluate our algorithm in separate convolutional layers and NNs.The results for convolutional layers show that, compared to baseline, the proposed algorithm speeds up the convolutional layer by an average of 1.42x, and the maximum speedup is up to 2.9x.Experiments on different NNs demonstrate that our algorithm can speed up the overall NN by up to 2.15x.
SPCAuth: Scalable and Privacy-Preserving Continuous Authentication for Web Applications

As password-based authentication fails to provide adequate security for online activities such as financial transactions, additional authentication factors are required. Such factors should provide both ease of use and an adequate level of privacy protection, while being easy to implement, operate and maintain, even for applications with thousands of users. As an approach meeting these requirements, we outline Scalable and Privacy- Preserving Continuous Authentication (SPCAuth). SPCAuth determines risk levels for actions that should be authenticated, without requiring explicit user interactions. It analyzes different aspects of user behavior by means of machine learning methods, while preserving the privacy of the affected individuals. SPCAuth trains only a single model per aspect of user behavior being considered, based on observations of all users, ensuring scalability and increasing accuracy for users with infrequent activities. In two experiments, we have confirmed that this key concept enables a scalable and accurate user authentication.
A Novel Information-Entropy-Based Feature Extraction Method for Transaction Fraud Detection

Machine learning methods are widely used in transaction fraud detection. The information-entropy-based feature extraction methods have been employed to improve the machine learning methods for fraud detection. However, the following two aspects have been ignored in the previous works. 1) Fixed sliding window makes the detection model lose effective information or learn irrelevant information. 2) Only the relationship among transactions of the individuals is considered while the interactions between individuals are ignored. In this paper, an adaptive information entropy (ADAIE) feature extraction method is proposed for transaction fraud detection, including an adaptive selection of window size and considering both individual and group behavior. The validity of ADAIE is tested on a real transaction dataset, and experimental results show the improvement of the proposed method.
Design and Implementation of College Music Teaching Management System

To solve the poor reusability and portability of traditional three-tier architecture teaching system, this paper first analyzes and models the system with UML, and provides the implementation process of music teaching management system based on J2EE platform and B/S architecture. The scheme uses MyEclipse 10 as the development tool, MSSQL2016 as the database management tool, and JSP as the front-end interface. It adopts JDBC to achieve asynchronous communication with the server, and finally puts forward a rich and friendly user interface in the web page, to provide users non-refresh visual experience. Finally, the function, performance and interface of the system are tested, and the results show that the system can get the expected effect, which also achieves all kinds of requirements in the demand analysis, and improves the efficiency and level of the whole university educational business.
A Reconstruction Attack Scheme on Secure Outsourced Spatial Dataset in Vehicular Ad-Hoc Networks

In the cloud-based vehicular ad-hoc network (VANET), massive vehicle information is stored on the cloud, and a large amount of data query, calculation, monitoring, and management are carried out at all times. The secure spatial query methods in VANET allow authorized users to convert the original spatial query to encrypted spatial query, which is called query token and will be processed in ciphertext mode by the service provider. Thus, the service provider learns which encrypted records are returned as the result of a query, which is defined as the access pattern. Since only the correct query results that match the query tokens are returned, the service provider can observe which encrypted data are accessed and returned to the client when a query is launched clearly, and it leads to the leakage of data access pattern. In this paper, a reconstruction attack scheme is proposed, which utilizes the access patterns in the secure query processes, and then it reconstructs the index of outsourced spatial data that are collected from the vehicles. The proposed scheme proves the security threats in the VANET. Extensive experiments on real-world datasets demonstrate that our attack scheme can achieve quite a high reconstruction rate.
Systematic Security Analysis of Stream Encryption With Key Erasure

We consider a generalized construction of stream ciphers with forward security. The design framework is modular: it is built from a so-called layer function that updates the key and (optionally) the nonce and generates a new pseudorandom output stream. We analyze the generalized construction for four different instantiations: two possible layer functions that are in turn instantiated with either a block cipher or a pseudorandom function. We prove that each of these instantiations gives a stream cipher that is pseudorandom and forward secure in the multi-user setting with a very tight bound. A comprehensive analysis shows that the two block cipher based instantiations achieve very similar bounds. For the pseudorandom function based instantiations there is no clear winner: either layer can be beneficial over the other one, depending on the choice of parameters. By instantiating the pseudorandom function with a generic construction such as the sum of permutations, we obtain a highly efficient and competitive stream cipher based on an n-bit block cipher that is secure beyond the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{\text {n}/2}$ </tex-math></inline-formula> birthday bound.
A Label Noise Robust Cross-Modal Hashing Approach

Cross-modal hashing has attracted more and more research interest for its high speed and low storage cost in solving cross-modal approximate nearest neighbor search problem. With the rapid growth of social networks, a large amount of information is generated every day, which inevitably contains some noisy information. However, most existing cross-modal hashing methods do not take label noise into consideration and simply assume that all training data are completely reliable. Therefore, these methods will be affected by label noise and reduce the effectiveness on the real-world data. In this paper, we propose a novel end-to-end cross-modal hashing method called label noise robust cross-modal hashing (LNRCMH) to solve the cross-modal hashing problem on data with label noise. LNRCMH first calculates the local outlier factor (LOF) for each instance to evaluate the probability that the instance is corrupted by the label noise. Then LNRCMH assigns lower weights to the instances with high probabilities to be corrupted. Finally, LNRCMH uses different neural networks to learn features for instances from different modalities and transforms them into binary hash codes. Our experimental results on multi-modal benchmark datasets demonstrate that LNRCMH performs significantly better than other related and comparable methods with noisy label annotations. Our approach also achieves competitive results in noise-free situations.
Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems

This article addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical. This problem is subject to the “curse of dimensionality” associated with the dynamic programming method. This article proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, “open-loop–closed-loop,” approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state-of-the-art algorithms.
A Novel IOT Based Smart Wheelchair Design for Cerebral Palsy Patients

Several patients face Cerebral Palsy. Such debilitating diseases impede motor control and make it difficult for them to operate traditional electric wheelchairs. Existing models of smart wheelchairs accommodate these issues to a certain extent but fail to deliver a solution for patients to use the wheelchairs completely autonomously. This paper proposes a novel model for a cost-effective smart wheelchair that takes simple gestures as input for movement, along with several quality-of-life and assistive modules such as vitals monitoring and voice memo support for patients suffering from memory loss, along with obstacle detection to ensure complete safety of the patient regardless of the terrain. The paper discusses the various modules present in the wheelchair, elaborates upon the algorithm used for input detection and calculation, and finally, the implementation of each module. Lastly, the paper enlists comparisons between existing smart wheelchair models and the proposed model and lists out its strengths, weaknesses and states its findings from the proposed system's results.
Team Performance in Large-Scale Agile Software Development

Agile ways of working are nowadays used in many software development departments in larger organizations. When scaling up agile ways of working, new practices for coordinating teams become necessary, and many organizations are implementing the Scaled Agile Framework. The added practices for coordinating teams could have an impact on team performance, but they have not been much studied. In this study, data were obtained by means of a survey questionnaire that was answered by 201 employees from three organizations: one from the automotive industry, one government agency, and a business bank. The study suggests that efficient inter-team coordination does not have a positive relationship to team performance, which is contrary to previous studies. However, results suggests that a high level of psychological safety has a significant positive correlation to team performance.
A Cloud Computing Resource Optimal Allocation Scheme Based on Data Correlation Analysis

The application of cloud computing is becoming more and more extensive, and the optimal allocation of resources can affect the quality and cost of cloud computing services. There are many factors that affect the optimal allocation of resources: providers focus on energy consumption, profit, SLA default, load balancing, resource utilization, etc.; consumers focus on QoS, cost, etc. It is necessary to analyze the factors that affect the optimal allocation of cloud computing resources from the perspectives of providers and consumers. We propose a cloud resource optimal allocation plan based on data correlation analysis.
Self-Regularity of Output Weights for Overparameterized Two-Layer Neural Networks

We consider the problem of finding a two-layer neural network with sigmoid, rectified linear unit, or binary step activation functions that “fits” a training data set as accurately as possible as quantified by the training error; and study the following question: does a low training error guarantee that the norm of the output layer (outer norm) itself is small? We address this question for the case of non-negative output weights. Using a simple covering number argument, we establish that under quite mild distributional assumptions on the input/label pairs; any such network achieving a small training error on polynomially many data necessarily has a well-controlled outer norm. Notably, our results (a) have a good sample complexity, (b) are independent of the number of hidden units, (c) are oblivious to the training algorithm; and (d) require quite mild assumptions on the data (in particular the input vector <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> ∊ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> need not have independent coordinates). We then show how our bounds can be leveraged to yield generalization guarantees for such networks.
Effectiveness Analysis of Different POS Tagging Techniques for Bangla Language

Parts-of-speech (POS) tagging plays an important role in the field of natural language processing (NLP), such as—retrieval of information, machine translation, spelling check, language processing, sentiment analysis, and so on. Many works have been done for Bangla part-of-speech (POS) tagging using machine learning but the result does not enough. It is a matter of fact that not even a single effective research work has been conducted for Bangla POS tagging using deep learning due to a lack of data scarcity. Considering that our context is the Bangla POS tagging employing both machine learning and deep learning approach. In our research, we have compared some well-known supervised POS tagging approaches (Brill, HMM, unigram, bigram, trigram, and recurrent neural network) for Bangla languages. The supervised POS tagging technique requires a large number of data set to tag accurately. That is why we have used a large number of data set for POS tagging of Bangla languages, which will accept a raw Bangla text to produce a Bangla POS tagged output that can be directly used for other NLP applications. After the comparison, we have found the best tagging approach in terms of performance. Bangla is an inflectional language. That is why it is a very much tough job for grammatical categories of Bangla language. But our proposed model works well for Bangla languages.
Automatic Recognition Method of Letter Images in English Self-Learning Based on Partial Differential Equation Method

According to the current situation of knowledge popularization, students simply rely on the knowledge learned in the classroom that is far from adapting to the development of modern society; so, every student needs to have the consciousness and ability of independent learning. The research of the English self-help learning system based on partial differential equation method comes into being with information network technology as the foundation for survival and development. The existing partial differential equation recognition models based on average curvature motion are all edge-based and need to use the external force defined by the image gradient to attract the zero level set (evolution curve) to move to the target edge and finally stay on the target edge. Therefore, it is difficult to obtain ideal results when extracting fuzzy or discrete boundaries (perceptual boundaries), and it is very sensitive to the selection of initial contour and noise. To solve this problem, this paper proposes a new recognition model of partial differential equations based on mean curvature motion. This overcomes some defects of existing edge models because it is region-based and does not require image gradient as a condition to stop evolution. The proposed model can avoid manual initial curve selection and allow stopping conditions to be set in the algorithm. In addition, in the numerical solution of partial differential equations, the existing model uses upwind difference scheme, and the semi-implicit additive operator separation method is adopted in this paper. Some other layers are added, and some hyperparameters are adjusted when the convolutional neural networks of inception PDEs are constructed by stacking the structure of inception PDEs. In the contrast experiment with the prototype, the software and hardware environment are the same, and the input is exactly the same. For the handwritten English alphabet data set, the variant structure can obtain more than 90% of the training accuracy and verification accuracy, which is better than the experimental accuracy of the prototype. In addition, because the inception PDE structure contains fewer parameters than the prototype, it is more computationally efficient and takes less training time per batch than the prototype.
A Contextual Study of Regulatory Framework for Blockchain

This study seeks to understand and explain the technological and regulatory challenges of blockchain technology particularly in execution mechanism of smart contracts as compared to regular contracts and to explore legal implication attached the blockchain technology. While evaluating the early days of regulatory framework of blockchain, the current study provides a focused review of relevant studies to identify the legal challenges arising from the application of AI in smart contracts and to find solutions to overcome these challenges. The study has emphasized certain areas related to the blockchain such as AI application and execution of smart contracts and finds that that there is currently a lack of legal certainty as to how various requirements of a valid contract would be satisfied. Hence, it highlights the need of regulation without disrupting the key yet essential features of blockchain. Keywords: Blockchain, Smart contract, AI, Framework, Legislation, Cryptocurrency
Semantics in High-Dimensional Space

Geometric models are used for modelling meaning in various semantic-space models. They are seductive in their simplicity and their imaginative qualities, and for that reason, their metaphorical power risks leading our intuitions astray: human intuition works well in a three-dimensional world but is overwhelmed by higher dimensionalities. This note is intended to warn about some practical pitfalls of using high-dimensional geometric representation as a knowledge representation and a memory model—challenges that can be met by informed design of the representation and its application.
The Transnational Cybercrime Extortion Landscape and the Pandemic: Changes in Ransomware Offender Tactics, Attack Scalability and the Organisation of Offending

The sudden disruption of work, recreation and leisure practices caused by the COVID-19 lockdown caught many organisations and their employees unaware, especially during the move towards working from home. This led adaptive cybercriminals to shift their own focus towards home workers as a way into organisational networks. The upshot was a massive acceleration in major cyberattacks upon organisations and a noticeable shift in offender tactics which scale up levels of fear in victims to encourage payment of the ransom. Such tactics include a shift towards naming and shaming victims, the theft of commercially sensitive data and attacks targeting organisations which provide services to other organisations. These developments have also led to changes in the organisation of offenders online. Such attacks negatively impact upon national and international economies as they try to recover from lockdown. Drawing upon an analysis of 4000+ cases of ransomware attacks collected for the EPSRC EMPHASIS & CRITICAL research projects, this paper charts the evolution of ransomware as a modern cybercrime and changes in the organisation of cyber-criminals as well as highlighting some of the implications for transnational policing.
Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots

As machine learning systems become more ubiquitous, methods for understanding and interpreting these models become increasingly important. In particular, practitioners are often interested both in what features the model relies on and how the model relies on them – the feature's impact on model predictions. Prior work on feature impact including partial dependence plots (PDPs) and Individual Conditional Expectation (ICE) plots has focused on a visual interpretation of feature impact. We propose a natural extension to ICE plots with ICE feature impact, a model-agnostic, performance-agnostic feature impact metric drawn out from ICE plots that can be interpreted as a close analogy to linear regression coefficients. Additionally, we introduce an in-distribution variant of ICE feature impact to vary the influence of out-of-distribution points as well as heterogeneity and non-linearity measures to characterize feature impact. Lastly, we demonstrate ICE feature impact's utility in several tasks using real-world data.
Relationship between Dwell-Time and Model Human Processor for Dwell-based Image Selection

We investigated the relationship between dwell-time and the model human processor (MHP). First, we devised an equation that can represent the time taken for recognizing an image based on MHP. Then, we evaluated whether the equation can represent the time and wheter the time estimated by the equation matches the user’s preferred dwell-time. The experiment consisted of two tasks: image selection with a button (button-task) and image selection with a dwell (dwell-task). From the results of the button-task, we found that the equation derived by MHP can estimate the time; the time taken for button selection was 662 ms on average, and the time estimated by the equation was 660 ms on average. Also, we showed that the estimated time represented the user’s preferred dwell-time; all participants in the experiment answered that 500 ms and 600 ms were their preferred dwell-times.