Reviewerly/exHarmony huggingface dataset:
A case study of delexical nouns in an EFL setting

This paper demonstrates the relevance of delexical nouns (way, end, case and point) based on corpus findings, course book analyses and classroom experiments. To this end, first, delexical noun functions and frequencies have been studied in-depth in two large English language corpora: Corpus of Contemporary American English (COCA) and British National Corpus (BNC). Additionally, the study reviewed the compound nouns' dictionary meanings. Two coursebooks, namely New English File and Face2Face, have been investigated to determine if delexical nouns are addressed in English language coursebooks to provide ideas to guide the learners in the classroom. Finally, a data-driven learning (DDL) experiment was carried out to assess the participants’ familiarity with the delexical nouns and their collocations. The results of the quantitative analysis from the study demonstrate that delexical nouns are introduced in the course books but are not explicitly explained. Consequently, there are no actual exercises to practise them. The classroom experiment findings reveal the participants’ positive attitude towards how the four delexical nouns, way, end, case and point, were introduced and taught.
Age estimation by extracting hierarchical age-related features

Image-based facial age estimation is considered an intractable problem because aging characteristics are hard to obtain. Most previous works have focused on extracting age-related features, but rarely explored which local region plays an important role. Several works combine local face regions with global face to estimate age in a heuristic way, where the local regions are uniformly cropped for each individual. In this paper, we design an individual adaptive segmentation of local regions of interest to perform personalized local features extraction and build hierarchical age features by erasing the local regions of interest iteratively for each individual. A joint multi-input and multi-output (MIMO) network for multi-task learning of age classification and regression tasks is designed by combining global features and personalized local features as inputs. In addition, we conduct extensive experiments to validate the effectiveness of the proposed method for age estimation, which beats most state-of-the-art methods in three public datasets and also works well for gender and race estimation.
Noise-boosted recurrent backpropagation

A statistical formulation of recurrent backpropagation (RBP) allows direct noise boosting for time-varying classification and regression. The noise boost reduces training iterations and improves accuracy. The injected noise is just that noise that makes the current signal more probable. This noise-boost result extends the two recent results that backpropagation is a special case of the generalized expectation maximization (EM) algorithm and that careful noise injection can always speed the average convergence of the EM algorithm to a local maximum of the log-likelihood surface. The noise-benefit conditions differ for additive and multiplicative noise in RBP. We tested noise-boosted RBP classifiers on 11 classes of sports video clips and tested RBP regressors on predicting the dollar-rupee exchange rate. Injecting noisy-EM (NEM) noise outperformed injecting blind noise or injecting no noise at all. Additive NEM noise usually outperformed multiplicative noise. The best case of NEM noise injection with RBP training of a recurrent neural classification model speeded up its training by 60% and improved its classification accuracy by 9.51% compared with noiseless RBP training and accuracy. The best performance of the NEM noise with the RBP training of a recurrent neural regression model yielded a 38% speed-up in training and also reduced the squared error by 49.3%. The injection of the additive NEM noise in the output and hidden neurons performed best.
Guard Cache: Creating False Cache Hits and Misses To Mitigate Side-Channel Attacks

Cache side-channel attacks have exposed serious security vulnerabilities in modern architectures. These attacks rely on measuring cache access times to determine if an access to an address is a hit or a miss in the cache. Such information can be used to identify which addresses were accessed by the victim, which in turn can be used to reveal or at least guess the information accessed by the victim. Mitigating the attacks while preserving the performance has been a challenge. The hardware mitigation techniques used in the literature include complex cache indexing mechanisms, partitioning cache memories, and hiding or undoing the effects of speculation. In this paper, we present a Guard Cache to obfuscate cache timing, making it more difficult for cache timing attacks to succeed. We create false cache hits by using the Guard Cache as a Victim Cache, and false cache misses by randomly evicting cache lines. Our obfuscations can be turned-on and turned-off on demand to protect critical sections or randomly to further obfuscate cache access times. We show that our false hits cause very minimal performance penalties ranging between −0.2% to 3.0% performance loss, while false misses can cause higher performance losses. We also show that our approach causes different number of cache hits and misses and different addresses causing misses when compared to traditional caches, demonstrating that common side-channel attacks such as Prime & Probe, Flush & Reload or Evict & Time are likely to misinterpret victims’ memory accesses. We use very small Guard Caches (1KiB-2KiB at L1 or 2KiB-4KiB at L2) requiring very minimal additional hardware. The hardware needed for random evictions is also minimal.
Autonomous Lending Organization on Ethereum with Credit Scoring

We propose the Autonomous Lending Organization on Ethereum (ALOE) system, which enables unsecured borrowing of funds on Ethereum. We incorporate a credit scoring approach that extends in the DeFi world the one that is used by traditional banks in order to quantify the risk of a borrower defaulting on a loan. As part of the loan process, first, we have a registration phase, where a notary verifies the real identity of a borrower and delegates to a set of auditors the task of storing a share of the real identity of a borrower to an Ethereum account while preserving anonymity. In the next phase, the Credit Bureau Smart Contract connects lenders to borrowers and updates credit scores. We automatically compute and update credit scores on-chain using the k-nearest neighbors algorithm.
Investigation and Countermeasure toward Unintentional Access to Docker Container

Due to the ease of management and the high performance of the containerization, many services have been deployed on container, e.g., Web server running in Docker. However, the Docker implementation suffers several fatal loopholes. In this paper, we perform a study on a persistent security problem of Docker, i.e., the port mapping statement results in a wrong IPTABLES rule, which has been disclosed for a while but is still not solved. Therefore, we are motivated to investigate and articulate this vulnerability with a technical explanation. Nevertheless, we proposed several solutions to address the problem. Further, we applied our network testbed for demonstrating the loophole and the effectiveness of the security solutions. We tested the performance of both attack and defense prototyping. The experimental results show that our approach not only increase the time cost for the attacker to identify the target but also bring negligible overhead for deploying the countermeasures.
Privacy-Preserving Trust Management For Vehicular Communications and Federated Learning

Cellular networking is evolving as a wireless technology to support a wide range of applications in vehicular communication. Cellular technologies enable vehicles to communicate with many applications to improve driving experience. All of these applications are not under the administration of the same authority. Secure Credential Management System (SCMS) is a Public Key Infrastructure (PKI) which provides certificates to vehicles to preserve vehicular privacy and supports many vehicular applications such as Basic Safety Messages (BSMs), misbehavior reporting, etc. On the other hand, privacy-preserving applications like federated learning (used to enable data-driven machine learning for better self-driving experience while protecting the vehicular data privacy) are outside of this SCMS-managed vehicular network and have separate PKI structure managed by a different authority. Since the trust management and PKI for vehicular vs. federated learning have been studied and developed separately, there is a need to establish trust between these two PKIs for secure communication between vehicles and Federated Learning Servers (FLSs). In this work, we demonstrate and analyze how these two orthogonal PKIs can establish trust with each other and thus, the end entities (vehicles and FLSs) can securely communicate with each other in an efficient manner to maintain vehicular privacy utilizing SCMS.
Multivariate Time Series Anomaly Detection with Deep Learning Models Leveraging Inter-Variable Relationships

This paper presents a system for multivariate time series anomaly detection using deep learning, with an added module to reflect variable relationships. The system uses an autoencoder to extract latent variables that reflect the time series characteristics of the variables, and calculates variable importance using the similarities among the variables. To evaluate the proposed method, experiments were conducted using three similarity measures: cosine similarity, distance correlation, and DTW. Four time series datasets were used for evaluation, and the results showed that the proposed model outperformed the baseline model in HAI 22.04 and HAI 21.03 datasets. For the WADI dataset, the Fl-score improved only when using cosine similarity, while the TaPR-Fl score improved only when using DTW. However, no performance improvement was observed in the SWaT dataset. These results suggest that the effectiveness of utilizing inter-variable relationships is dependent on the characteristics of the data and the similarity calculation method employed. Therefore, a careful selection of the appropriate similarity calculation method for a given dataset is necessary to achieve optimal performance improvements.
OFMCDM/IRF: A Phishing Website Detection Model based on Optimized Fuzzy Multi-Criteria Decision-Making and Improved Random Forest

With increasing social and financial activities on the web, phishing has become one of the most critical threats in cybersecurity. Many methods have been proposed to identify phishing websites, such as fuzzy logic, neural networks, data mining, heuristic-based phishing detection, and machine learning. On the other hand, phishers develop more sophisticated techniques, decreasing the efficacy of the existing methods. This paper proposes a phishing detection model based on optimized Fuzzy Multi-Criteria Decision-Making (OFMCDM) and Improved Random Forest (IRF). The model utilizes Uniform Resource Locator (URL) and Hypertext Markup Language (HTML) features to prevent sharing users’ sensitive information such as username, password, social security, or credit card number. Our experiments show competitive results from our models compared to existing models, including Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree.
WebTracker: Real Webbrowsing Behaviors

With increased privacy concerns, anonymity tools such as VPNs and Tor have become popular. However, the packet metadata such as the packet size and number of packets can still be observed by an adversary. This is commonly known as fingerprinting and website fingerprinting attacks have received a lot of attention recently as a known victim’s website visits can be accurately predicted, deanonymizing that victim’s web usage. Most of the previous work have been performed in laboratory settings and have made two assumptions: 1) a victim visits one website at a time, and 2) the whole website visit with all the network packets can be observed. To validate these assumptions, a new private webbrowser extension called WebTracker is deployed with real users. WebTracker records the websites visited, when the website loading starts, and when the website loading finishes. Results show that users’ browsing patterns are different than what was previously assumed. Users may browse the web in a way that acts as a countermeasure against website fingerprinting due to multiple websites overlapping and downloading at the same time. Over 15% of websites overlap with at least one other website and each overlap was 66 seconds. Moreover, each overlap happens roughly 9 seconds after the first website download has started. Thus, this reinforces some previous work that the beginning of a website is more important than the end for a website fingerprinting attack.
Lightweight and Effective Website Fingerprinting Over Encrypted DNS

The DNS over HTTPS (DoH) protocol is implemented to improve the original DNS protocol that uses unencrypted DNS queries and responses. With the DNS traffic, an eavesdropper can easily identify websites that a user is visiting. In order to address this concern of web privacy, encryption is used by performing a DNS lookup over HTTPS. In this paper, we studied whether the encrypted DoH traffic could be exploited to identify websites that a user has visited. This is a different type of website fingerprinting by analyzing encrypted DNS network traffic rather than the network traffic between the client and the web server. DNS typically uses fewer network packets than a website download. Our model and algorithm can accurately predict one out of 10, 000 websites with a 95% accuracy using the first 50 DoH packets. In the open-world environment with 100, 000 websites, our model achieves an F1-score of 93%.
Formalizing Path Explosion for Recursive Functions via Asymptotic Path Complexity

Path coverage is of critical importance in software testing and verification. Furthermore, path explosion is a well-known challenge for automatic software analysis techniques like symbolic execution. Asymptotic Path Complexity (APC) is a code complexity metric that formalizes the quantitative measurement of path explosion and therefore measures the difficulty of achieving path coverage. Prior APC methods were not sufficiently expressive to model the path explosion behavior in recursive functions. Existing analyses simply ignored recursive calls and were unable to correctly compute APC for recursive functions. We present a novel method for computing asymptotic path complexity for recursive functions, which we call APC-R. Our approach is based on the analytic combinatorics of context-free grammars. We implemented our approach on top of the code complexity analysis tool METRINOME and evaluated our implementation on a set of benchmark programs written in the C programming language. Our experiments demonstrate that APC-R is a sound upper bound on the growth rate of the number of program paths explored as a function of increasing exploration depth when generating tests with the KLEE symbolic execution engine. Our APC-R implementation provides sound bounds on KLEE’S symbolic execution path explosion for recursive functions in cases where the original APC implementation did not, and APC-R matches the results of APC for non-recursive functions with only marginal computational overhead. That is, APC-R subsumes earlier APC work without significant performance cost.
A Dafny-based approach to thread-local information flow analysis

The Dafny program verifier supports proofs of functional correctness of single-threaded programs written in an imperative, object-based or functional style. In this paper, we show how Dafny can also be used to support proofs of information flow security in multi-threaded programs. For generality, information flow is analysed with respect to a user-defined lattice of security values, and the security classifications of program variables are value-dependent, i.e., they are not fixed but depend on the current program state. For scalability, our multi-threaded analysis is carried out thread locally using rely/guarantee reasoning. The required well formedness properties of our security lattices and rely and guarantee conditions are proven using Dafny lemmas.
Goal Controller Synthesis for Self-Adaptive Systems

Much like humans, a growing number of software-controlled systems must cope with uncertainty and disruption through self-adaptation. However, while humans achieve this feat by changing both the means through which they pursue their goals and-when unavoidable-the goals themselves, self-adaptive systems are often only using the former adaptation mechanism. In this ‘research ideas’ paper, we argue that exploiting the latter mechanism is equally important, and we propose a new goal modelling paradigm that supports reasoning about goal change, and the use of probabilistic model checking tools to synthesise goal-management control software for self-adaptive systems.
Data Communication Systems Protocol Stacks

5G NR, Wi-Fi, and Bluetooth communications are all data based where the data (stream of bits, a bit being either a 1 or a 0) is sent in discrete packets, each packet being a sequence of bytes (each byte contains 8 bits). Packet data communication systems employ so-called protocols. Here a protocol is a set of rules that must be followed for two devices to communicate with each other. Protocols are layered on top of each other to form a communication architecture referred to as a protocol stack. Each protocol provides a function or functions required to make data communication possible. Typically, many protocols are used so that overall functioning can be broken down into manageable pieces. 5G and Wi-Fi networks communicate via the Internet. The Internet communicates with the aid of a suite of software network protocols called Transmission Control Protocol/Internet Protocol (TCP/IP) or User Datagram Protocol/Internet Protocol (UPD/IP). TCP/IP and UDP/IP work together with a data link layer and a physical layer (these latter layers combined are referred to as the Network Access Layer) to create an effective packet switched network for Internet communications. In this chapter, a brief introduction to TCP, UDP, IP, data link, and physical layer protocols is presented.
Conclusions and Future Work

This chapter summarizes the book by recapitulating the key points of every chapter and by answering the five research questions posed in Chap. 1 . The limitations of the approach and the current implementation of the conversation simulation are discussed, and finally, future work on the conversation simulation approach is outlined.
Fundamentals

This chapter describes the foundations and related work of speech quality, dialogue systems, and dialogue simulation. The fundamentals in speech quality focus on the two technical impairments delay and packet loss, as well as the assessment of conversational quality, which includes the parametric conversation analysis method. Also, an introduction to instrumental speech quality prediction, with a focus on the E-model, will be given. The fundamentals in spoken dialogue systems will outline the use cases of dialogue simulation and the architectural details needed for the implementation of a conversation simulation.
Discussion forum for the learning of Modern Physics in high school Mexico

Abstract This research refers to the B-Learning educational model using the Moodle platform in the learning of Relativistic Physics as a complement to face-to-face classes in the high school modality, seeking mainly to stimulate the construction of the student’s own knowledge, oriented towards the autonomy of the student The activity was carried out through the implementation of a discussion forum with a sample of 20 high school students. The methodology is of a qualitative nature, which refers to the analysis of the data with the Atlas.ti platform, considering the nuclear variable of communication and the associated codes of significant interaction namely, guided didactic conversation, social interaction, open communication, simulated communication and discussion. The platform allows us to recognize the relevance of communication towards student learning through a discussion forum, that is confirmed through the correlations found between the codes. The results obtained through this analysis indicate the type of communication that students have when they participate in a forum for understanding the topic of Relativity, categorized with the codes, and that reveals the exchange that may or may not be significant about the content that is proposed in the discussion. Although more solid arguments are lacking among the students, the importance of communication as one of the competences of the 21st century is developed, and, above all, the close accompaniment of the teacher with recommendations and proposals for searching for information. In addition, the proposed evaluation allows the teacher to recognize the points where his contribution should be reinforced and, above all, lead to a self-assessment of the students.
Bayesian Heterogeneous Hidden Markov Models with an Unknown Number of States

AbstractHidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. However, prior knowledge about the order is often unavailable, and a pairwise comparison using criterion-based methods becomes increasingly tedious and computationally demanding when the model space enlarges. A few studies have considered simultaneously performing order selection and parameter estimation under the frequentist framework. Still, they focused only on homogeneous HMMs and thus cannot accommodate situations where potential covariates affect the between-state transition. This study proposes a Bayesian double-penalized (BDP) procedure to conduct a simultaneous order selection and parameter estimation for heterogeneous HMMs. We develop a novel Markov chain Monte Carlo algorithm coupled with an efficient adjust-bound reversible jump scheme to address the challenges in updating the order. Simulation studies show that the proposed BDP procedure considerably outperforms the commonly used criterion-based methods. An application to the Alzheimer's Disease Neuroimaging Initiative study further confirms the utility of the proposed method. Supplementary materials for this article are available online.KEYWORDS: Bayesian methodDouble penalizationDynamic heterogeneityLongitudinal dataOrder selection Supplementary MaterialsSupplementary Material: The supplemental files include the Appendix which gives the proof of the propositions in Section 3, full conditional distributions in Section 4, and additional numerical results in Sections 5 and 6. (BHMM Supp.pdf)R code: The supplemental files for this article include R programs which can be used to replicate the simulation study included in the article. Please read file README contained in the zip file for more details. (program package.zip)AcknowledgmentsThe authors are thankful to the editor, the associate editor, and two anonymous reviewers for their valuable comments and suggestions, which have helped improve the article substantially.Disclosure StatementThe authors report there are no competing interest to declare.Additional informationFundingThis research was fully supported by GRF Grants (14302519, 14302220) from Research Grant Council of the Hong Kong Special Administration Region.