Reviewerly/exHarmony huggingface dataset:
Undergraduate Students’ Perception of the Use of WhatsApp-Based Speaking Instructional Materials

WhatsApp-based speaking instructional material (WABSIM), a social media-integrated teaching and learning form, is a much-needed and highly demanded speaking resource for students to access and learn English in flexible ways. This study was conceived to figure out students’ responses to the use of WABSIM for learning basic speaking. Twenty eight English Education Department students filled out a Google Forms questionnaire assessing the indicators of experience, motivation, and attitude. This current study reveals the perception of use of WABSIM; in terms of experience, 64.3% of students agree that it has helped enrich their insight, and 53.6% of students agree that it promotes an enjoyable learning situation. In the motivation aspect, 57.1% of students agree that they feel motivated in studying speaking skills using the WABSIM, 53.5% of students enjoy the WhatsApp-based communication tasks, 67.9% are willing to study speaking using WABSIM, and 50% agree that speaking lessons during the semester were fun. In terms of attitude indicators, half of the students agree that the WABSIM could increase their self-confidence in speaking English about daily topics, and that they became more active in learning – it helped them to learn the course independently and collaboratively. Therefore, the research indicates that students favourably perceive the use of WABSIM for learning in flexible ways inside and beyond the classroom, and that the learning through the WhatsApp instructions, such as recording audio and video material, as well as making audio and video calls, encourages students to be collaborative and even more independent.Keywords: WhatsApp, social media, speaking skill, mobile learning, motivation, students’ perceptions
A Study of a Confucius Culture Learning Environment Based on a 3D Metaverse

With the development of technology, the term metaverse is becoming more known. Metaverses hope to turn imagination into reality through the integration of various technologies. Metaverses are the latest stage in the development of visual immersive technology with four characteristics: spatio-temporality, authenticity, independence, and connectivity. The purpose of this paper is to study the construction of a Confucian culture learning environment in a 3D metaverse, where students connect to a platform constructed by a 3D virtual world through a Head-Mounted Display (HMD). Students can learn about Confucianism, Confucius’ life and stories, as well as visit Confucian cultural buildings. The platform is based on a virtual scene built on The Sims 4, and the HMD is used as a connection between the metaverse and the real world enriching the user's experience with the same features and perceptions as the real world, which will make the online learning approach more meaningful. Finally, this paper presents the content of the metaverse exhibition through an experimental approach.
Mercury: Constant-Round Protocols for Multi-Party Computation with Rationals

Most protocols for secure multi-party computation (MPC) work over fields or rings, which means that encoding techniques are needed to map rational-valued data into the algebraic structure being used. Leveraging an encoding technique introduced in recent work of Harmon et al. that is compatible with any MPC protocol over a prime-order field, we present Mercury—a family of protocols for addition, multiplication, subtraction, and division of rational numbers. Notably, the output of our division protocol is exact (i.e., it does not use iterative methods). Our protocols offer improvements in both round complexity and communication complexity when compared with prior art, and are secure for a dishonest minority of semi-honest parties.
On the Privacy-Preserving Infrastructure for Authenticated Key Exchange

Privacy-preserving authenticated key exchange (PPAKE) protocols aim at providing both session key indistinguishability and party identifier hiding. Parties in PPAKEs usually interact with a public key infrastructure (PKI) or similar services for authentication, especially for validating certificates and other identity-binding tokens during the handshake. However, these essential validation messages, which have not been captured in current models, open attack surfaces for adversaries. In this paper, we propose a new refined infrastructure model (RI) for privacy in the infrastructure. As the cryptographic core, we also present a novel certificate validation protocol (CVP) that can be instantiated with anonymous Bloom filter key encapsulation mechanisms ( ). The new CVP protects user identity in certificate validation, thus enhances the privacy guarantee of PPAKE.
Permutation-Based Deterministic Authenticated Encryption with Minimum Memory Size

Deterministic authenticated encryption (DAE) provides data integrity and authenticity with certain robustness. Previous DAE schemes for low memory are based on block ciphers (BCs) or tweakable block ciphers (TBCs), which can be implemented with 3s bits of memory for s-bit security. On the other hand, schemes based on cryptographic permutations have attracted many researchers and standardization bodies. However, existing permutation-based DAEs require at least 4s bits, or even 5s bits of memory. In this paper, $$\textsf{PALM}$$ , a new permutation-based DAE mode that can be implemented only with 3s bits of memory is proposed, implying that permutation-based DAEs achieve a competitive memory size with BC- and TBC-based DAEs. Our hardware implementation of $$\textsf{PALM}$$ , instantiated with PHOTON $$_{256}$$ for 128-bit security, achieves 3,585 GE, comparable with the state-of-the-art TBC-based DAE. Finally, optimality of 3s bits of memory of $$\textsf{PALM}$$ is shown.
TC4SE: A High-Performance Trusted Channel Mechanism for Secure Enclave-Based Trusted Execution Environments

We present TC4SE, a trusted channel mechanism suitable for secure enclave-based trusted execution environments, such as Intel SGX, that leverages the existing security properties provided by the TEE remote attestation scheme and Transport Layer Security (TLS) protocol. Unlike previous works that integrate attestation into the TLS handshake, TC4SE separates these two processes and binds the trust to the authentication primitives used by the TLS protocol. TC4SE avoids modifying the TLS protocol itself, thereby avoiding extra overhead, dependencies, and inadvertent introduction of security vulnerabilities. We argue that TC4SE provides the same level of security assurance as related works, while offering superior performance and implementation advantages, comparable to the regular TLS protocol.
An Improved BKW Algorithm for Solving LWE with Small Secrets

The Blum–Kalai–Wasserman (BKW) algorithm is an important combinatorial algorithm for solving the Learning With Errors (LWE) problem. In this paper, we focus on the LWE problem with small secrets and present an improved BKW algorithm. BKW algorithm has two phases, the reduction phase and the solving phase and our new algorithm uses new techniques to optimize both of them. For the first phase, we combine the modulus switching technique with coding theory and for the second one, we use a new pruning guessing strategy for the small secrets. To the best of our knowledge, our algorithm is currently the fastest BKW-style algorithm for solving LWE with small secrets. The bit-security of our new algorithm reduces by 6–19 bits compared with the most efficient and commonly used BKW algorithm introduced in [CRYPTO’15].
Load-and-Act: Increasing Page Coverage of Web Applications

Current solutions for automated vulnerability discovery increase coverage but typically do not interact with the web application. Thus, vulnerabilities in code for handling user interactions often remain undiscovered. This paper evaluates interactive strategies that simulate user interaction to increase client-side JavaScript code coverage. We exemplarily analyze 5 widely deployed, real-world web applications and find that simple random walks can double the number of covered branches compared to merely waiting for the page to be loaded (“load-and-wait”). Additionally, we propose novel approaches relying on state-independent models and demonstrate that these outperform the non-interactive baseline by $$2.4{\times }$$ in terms of covered branches and $$3.1{\times }$$ in terms of discovered data flows. Our interactive strategies have revealed a client-side data flow in SuiteCRM that is exploitable as a stored XSS and SSRF attack but cannot be found without user interaction.
Exploring Privacy-Preserving Techniques on Synthetic Data as a Defense Against Model Inversion Attacks

In this work, we investigate privacy risks associated with model inversion attribute inference attacks. Specifically, we explore a case in which a governmental institute aims to release a trained machine learning model to the public (i.e., for collaboration or transparency reasons) without threatening privacy. The model predicts change of living place and is important for studying individuals’ tendency to relocate. For this reason, it is called a propensity-to-move model. Our results first show that there is a potential leak of sensitive information when a propensity-to-move model is trained on the original data, in the form collected from the individuals. To address this privacy risk, we propose a data synthesis + privacy preservation approach: we replace the original training data with synthetic data on top of which we apply privacy preserving techniques. Our approach aims to maintain the prediction performance of the model, while controlling the privacy risk. Related work has studied a one-step synthesis of privacy preserving data. In contrast, here, we first synthesize data and then apply privacy preserving techniques. We carry out experiments involving attacks on individuals included in the training data (“inclusive individuals”) as well as attacks on individuals not included in the training data (“exclusive individuals”). In this regard, our work goes beyond conventional model inversion attribute inference attacks, which focus on individuals contained in the training data. Our results show that a propensity-to-move model trained on synthetic training data protected with privacy-preserving techniques achieves performance comparable to a model trained on the original training data. At the same time, we observe a reduction in the efficacy of certain attacks.
Balanced Privacy Budget Allocation for Privacy-Preserving Machine Learning

The preservation of privacy during the learning phase of machine learning is challenging. There are two methods to achieve privacy-preserving machine learning: adding noise to machine-learning model parameters, which is often selected for its higher accuracy; and executing learning using noisy data, which is preferred for privacy. Recently, a Scalable Unified Privacy-preserving Machine learning framework ( $$\mathsf SUPM$$ ) has been proposed, which controls the balance between privacy and accuracy by harmonizing the privacy mechanisms used in dimension reduction, training and testing phases. This paper proposes a novel method that allocates privacy budgets according to their effectiveness that improves the accuracy without sacrificing the number of available attributes. Our privacy budget allocation algorithm can be applied into $$\mathsf SUPM$$ and improve the accuracy while keeping the privacy. We evaluate its performance using logistic regression and support vector machines as machine learning algorithms. $$\mathsf SUPM$$ using our privacy budget allocation algorithm is effective in terms of accuracy and the number of available attributes. We also clarify the conditions under which our method is more effective for a given dataset.
Unlocking Potential: The Smart Investment in Education Management for Future Success in Bangladesh

In the dynamic context of Bangladesh, where education serves as a cornerstone of societal progress, this article explores the imperative of strategic investment in education management. This investment holds the key to unlocking the nation's latent potential and propelling it towards a future marked by success and sustainable development. Bangladesh's education landscape has seen notable advancements, yet challenges like high dropout rates and outdated curricula persist. The essence of effective education management lies in addressing these challenges comprehensively. By modernizing curricula, prioritizing teacher training, integrating technology, refining assessment techniques, and embracing inclusive education, Bangladesh can lay the groundwork for a transformative educational experience. Investing in education management carries numerous benefits. Firstly, it acts as a catalyst for economic growth and innovation, as a skilled and educated workforce that attracts investments and fosters a culture of innovation. Secondly, it promotes social mobility by democratizing access to quality education, enabling individuals from diverse backgrounds to ascend the socio-economic ladder. Additionally, focusing on education management enhances global competitiveness by producing professionals capable of thriving in international markets. Moreover, this investment aligns with sustainable development goals, empowering individuals to contribute meaningfully to their communities. While the Bangladeshi government's commitment is palpable through policies like the National Education Policy, the collaboration of public and private sectors is crucial. Private investment can infuse resources into areas such as infrastructure development, technology integration, and teacher training, augmenting governmental initiatives. In conclusion, this article underscores the significance of investing wisely in education management to shape Bangladesh's future. By amalgamating curriculum innovation, teacher development, technology adoption, and inclusive education, Bangladesh can harness its potential and emerge as a beacon of success on the global stage. This collective effort, embracing education as a cornerstone, promises not only individual success but also the collective advancement of the nation.
The Evolution of the Post-Quantum Cyber Environment

Abstract: Quantum computers are expected to one day be capable of breaking public key cryptography, enabling a step change in cyberwarfare capabilities. Risk can be mitigated using new quantum-resistant cryptography algorithms, but the international migration to a new cryptography standard will be time-consuming, costly, and dependent on the work of international standards-developing organizations. Technical standards are increasingly a focus of geopolitical competition, as China has attempted to manipulate standards organizations to tilt standards in their favor. Contrasting national approaches to this competition could significantly impact the evolution of the post-quantum cyber environment. The United States should continue its domestic efforts to migrate to a new cryptography standard, pursue international standardization, and maintain a measured strategy to counter interference from China in open standardization processes.
CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets

Automated segmentation of ultrasound images can assist medical experts with diagnostic and therapeutic procedures. Although using the common modality of ultrasound, one typically needs separate datasets in order to segment, for example, different anatomical structures or lesions with different levels of malignancy. In this paper, we consider the problem of jointly learning from heterogeneous datasets so that the model can improve generalization abilities by leveraging the inherent variability among datasets. We merge the heterogeneous datasets into one dataset and refer to each component dataset as a subgroup. We propose to train a single segmentation model so that the model can adapt to each sub-group. For robust segmentation, we leverage recently proposed Segment Anything model (SAM) in order to incorporate sub-group information into the model. We propose SAM with Condition Embedding block (CEmb-SAM) which encodes sub-group conditions and combines them with image embeddings from SAM. The conditional embedding block effectively adapts SAM to each image sub-group by incorporating dataset properties through learnable parameters for normalization. Experiments show that CEmb-SAM outperforms the baseline methods on ultrasound image segmentation for peripheral nerves and breast cancer. The experiments highlight the effectiveness of CEmb-SAM in learning from heterogeneous datasets in medical image segmentation tasks. The code is publicly available at https://github.com/DongDong500/CEmb-SAM
Task-Driven Prompt Evolution for Foundation Models

Promptable foundation models, particularly Segment Anything Model (SAM) [3], have emerged as a promising alternative to the traditional task-specific supervised learning for image segmentation. However, many evaluation studies have found that their performance on medical imaging modalities to be underwhelming compared to conventional deep learning methods. In the world of large pre-trained language and vision-language models, learning prompt from downstream tasks has achieved considerable success in improving performance. In this work, we propose a plug-and-play Prompt Optimization Technique for foundation models like SAM (SAMPOT) that utilizes the downstream segmentation task to optimize the human-provided prompt to obtain improved performance. We demonstrate the utility of SAMPOT on lung segmentation in chest X-ray images and obtain an improvement on a significant number of cases ( $$\sim {75}\%$$ ) over human-provided initial prompts. We hope this work will lead to further investigations in the nascent field of automatic visual prompt-tuning.
Interpreting Intrusions - The Role of Explainability in AI-Based Intrusion Detection Systems

Machine learning has become a key component of the effective detection of network intrusions. Yet, it comes with the lack of transparency - an issue which can be mitigated with the employment of explainable AI techniques. In this paper, the crucial role of explainability in intrusion detection is discussed, along with its benefits and drawbacks, followed by presenting and comparing the results of four main explainability techniques applied to an intrusion detection system.
Exploring Text-Guided Synthetic Distribution Shifts for Robust Image Classification

The empirical risk minimization approach of contemporary machine learning leads to potential failures under distribution shifts. While out-of-distribution data can be used to probe for robustness issues, collecting this at scale in the wild can be difficult given its nature. We propose a novel method to generate this data using pretrained foundation models. We train a language model to generate class-conditioned image captions that minimize their cosine similarity with that of corresponding class images from the original distribution. We then use these captions to synthesize new images with off-the-shelf text-to-image generative models. We show our method’s ability to generate samples from shifted distributions, and the quality of the data for both robustness testing and as additional training data to improve generalization.
Knowledge Distillation with Relative Representations for Image Representation Learning

Relative representations allow the alignment of latent spaces which embed data in extrinsically different manners but with similar relative distances between data points. This ability to compare different latent spaces for the same input lends itself to knowledge distillation techniques. We explore the applicability of relative representations to knowledge distillation by training a student model such that the relative representations of its outputs match the relative representations of the outputs of a teacher model. We test our Relative Representation Knowledge Distillation (RRKD) scheme on supervised and self-supervised image representation learning with MNIST and show that an encoder can be compressed to 47.71% of its original size while maintaining 91.92% of its full performance. We demonstrate that RRKD is competitive with or outperforms other relation-based distillation schemes in traditional distillation setups (CIFAR-10, CIFAR-100, SVHN) and in a transfer learning setting (Stanford Cars, Oxford-IIIT Pets, Oxford Flowers-102). Our results indicate that relative representations are an effective signal for knowledge distillation. Code is made available at https://github.com/Ramos-Ramos/rrkd .
Incremental Extreme Learning Machine for Binary Data Stream Classification

Classifier ensembles have shown the ability to classify drifted data streams. The following paper proposes an ensemble consisting of a single hidden layer feedforward neural network and an Extreme Learning Machine. For this purpose, a new incremental version of the Extreme Learning Machine is also proposed. Motivations behind such an approach have been precisely described and supported by conducted research. The achieved results show when the architecture might be most useful and what are the possible directions for future development of this method.
Quillbot Web-Application: Utilizing Online Technology on Academic Writing at an Indonesian Islamic Higher Education

The objectives of this research were to find out the impact and obstacles that QuillBot's utilization as an online technology faced on students’ academic writing. This research used a phenomenological qualitative design. The subjects were eight students who participated in this research. This research used an interview guide to collect the data. The result showed that the impact of QuillBot as an online technology on students’ academic writing has a positive and negative impact, where the positive is that QuillBot can help students save their time to write the academic writing process, increase their vocabulary, and help in the writing process with the complexity, objectivity, formality, and hedging aspects of academic writing itself. The negative impact indicates the students’ dependence on QuillBot tools, where they become lazy to think and do not use their ability to write their academic writing. Finally, there were the obstacles that students encountered while using QuillBot, such as a bad network and a lack of clear instructions on how to upgrade to the premium version.
Politeness Strategies Used by Male and Female Teachers in English Classroom Interaction at SMK Nasional Makassar

This research aimed to finding out (1) What are courteousness methodologies utilized by male in classroom interaction at SMK Nasional Makassar, (2) What are courteousness methodologies utilized by female instructors in classroom interaction at SMK Nasional Makassar, (3) What are the most dominant politeness techniques utilized by male and female instructors in classroom interaction at SMK Nasional Makassar. There are 4 English teachers including 2 male teachers and 2 female teachers at SMK Nasional who participated. This study using a qualitative descriptive method. Data research was collected through observation. Based on the results researcher found that politeness strategies used by male were thirty-four utterances used by male teachers from the total utterances were eighty-eight. The researcher found twenty-one utterances as bold on record, seven utterances as positive politeness, five utterances as off-record and one utterance as Say-Nothing. Moreover, politeness strategies utilized by teachers based on the results found that there were fifty-four utterances used by female teachers from the total utterances were eighty-eight. In addition, there were thirty-three utterances as bold on record. Five utterances as off-record. Seven utterances as positive politeness. Nine utterances as Say-Nothing strategy. The most dominant courteousness techniques utilized by male and female teachers in English classroom at SMK Nasional Makassar was bold on-record. It can conclude that the teachers have close relationship with his students.