Reviewerly/exHarmony huggingface dataset:
Measurement-device-independent quantum key distribution with leaky sources

Abstract Measurement-device-independent quantum key distribution (MDI-QKD) can remove all detection side-channels from quantum communication systems. The security proofs require, however, that certain assumptions on the sources are satisfied. This includes, for instance, the requirement that there is no information leakage from the transmitters of the senders, which unfortunately is very difficult to guarantee in practice. In this paper we relax this unrealistic assumption by presenting a general formalism to prove the security of MDI-QKD with leaky sources. With this formalism, we analyze the finite-key security of two prominent MDI-QKD schemes—a symmetric three-intensity decoy-state MDI-QKD protocol and a four-intensity decoy-state MDI-QKD protocol—and determine their robustness against information leakage from both the intensity modulator and the phase modulator of the transmitters. Our work shows that MDI-QKD is feasible within a reasonable time frame of signal transmission given that the sources are sufficiently isolated. Thus, it provides an essential reference for experimentalists to ensure the security of implementations of MDI-QKD in the presence of information leakage.
Permasalahan Budaya Membaca di Indonesia (Studi Pustaka Tentang Problematika &amp; Solusinya)

Reading is one of the language skills that must be mastered by every student, the ability to read becomes one of the mandatory components in the national education curriculum. However, assessment programs of various international institutions and the Ministry of Education &amp; Culture show that students in Indonesia are still low in their reading ability, in this case reading comprehension skills. For this reason, through this literature study, the problems and solutions are examined based on various library sources. The results of the literature study show that there are different standards between the teaching of reading in education in Indonesia and the standards used by various international institutions in measuring students' reading skills. In addition there are various factors driving the low ability &amp; reading culture of students in Indonesia, namely: 1) Reading has not become a necessity, let alone become a culture, 2) Misperception about the concept of reading ability in most societies including students and teachers, 3) Capacity building reading is still perceived as part of the responsibility of language courses only, 3) The learning process still does not utilize models, methods, learning strategies that are diverse and suitable for reading comprehension learning, 4) reading material, learning activities and practice / evaluation questions there are teaching materials in schools that tend to still dwell on low order thinking skills, 5) Not yet the maximum facilities and infrastructure of school libraries as a center for developing the ability and culture of reading students, and 6) Program of Gerakan Literasi Sekolah (GLS) has not shown the progress expected to build a reading culture.
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenza-like illness (ILI) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.
On the Equivalence between Positional Node Embeddings and Structural Graph Representations

This work provides the first unifying theoretical framework for node embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that relationship between structural representations and node embeddings is analogous to that of a distribution and its samples. We prove that all tasks that can be performed by node embeddings can also be performed by structural representations and vice-versa. We also show that the concept of transductive and inductive learning is unrelated to node embeddings and graph representations, clearing another source of confusion in the literature. Finally, we introduce new practical guidelines to generating and using node embeddings, which further augments standard operating procedures used today.
Improving whale optimization algorithm for feature selection with a time-varying transfer function

&lt;p style='text-indent:20px;'&gt;Feature selection is a valuable tool in supervised machine learning research fields, such as pattern recognition or classification problems. Feature selection used to eliminate irrelevant and noise features that adversely affect results. Swarm algorithms are usually used in feature selection problem; these algorithms need transfer functions that change search space from continuous to the discrete. However, transfer functions are the backbone of all binary swarm algorithms. Transfer functions in the current formula cannot provide binary swarm algorithms with a fit balance between exploration and exploitation stages. In this work, a feature selection approach based on the binary whale optimization algorithm with different kinds of updating techniques for the time-varying transfer functions is proposed. To evaluate the performance of the proposed method, three of each chemical and biological binary datasets are used. The results proved that BWOA-TV2 has consistency in feature selection and it gives rise to the high accuracy of the classification with more congruent in the convergence. It worth mentioning that the proposed method is proved advance in performance over competitor optimization algorithms, such as particle swarm optimization (PSO) and firefly optimization (FO) that commonly used in this field.&lt;/p&gt;
Boosting API Recommendation With Implicit Feedback

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of users' feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting users' feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection

In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. This is a common goal in predictive maintenance, which aims to forecast the imminent faults of an appliance given abundant samples of normal behaviors. Local outlier factor (LOF) is one of the state-of-the-art models used for anomaly detection, but the predictive performance of LOF depends greatly on the selection of hyperparameters. In this paper, we propose a novel, heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model that uses the proposed method shows good predictive performance in both simulations and real data sets.
Hybrid Machine Learning EDFA Model

A hybrid machine learning (HML) model combining a-priori and a-posteriori knowledge is implemented and tested, which is shown to reduce the prediction error and training complexity, compared to an analytical or neural network learning model.
Carnus: Exploring the Privacy Threats of Browser Extension Fingerprinting

With users becoming increasingly privacy-aware and browser vendors incorporating anti-tracking mechanisms, browser fingerprinting has garnered significant attention.Accordingly, prior work has proposed techniques for identifying browser extensions and using them as part of a device's fingerprint.While previous studies have demonstrated how extensions can be detected through their web accessible resources, there exists a significant gap regarding techniques that indirectly detect extensions through behavioral artifacts.In fact, no prior study has demonstrated that this can be done in an automated fashion.In this paper, we bridge this gap by presenting the first fully automated creation and detection of behavior-based extension fingerprints.We also introduce two novel fingerprinting techniques that monitor extensions' communication patterns, namely outgoing HTTP requests and intra-browser message exchanges.These techniques comprise the core of Carnus, a modular system for the static and dynamic analysis of extensions, which we use to create the largest set of extension fingerprints to date.We leverage our dataset of 29,428 detectable extensions to conduct a comprehensive investigation of extension fingerprinting in realistic settings and demonstrate the practicality of our attack.Our in-depth analysis confirms the robustness of our techniques, as 83.6% -87.92% of our behavior-based fingerprints remain effective against a state-of-the-art countermeasure.Subsequently, we aim to explore the true extent of the privacy threat that extension fingerprinting poses to users, and present a novel study on the feasibility of inference attacks that reveal private and sensitive user information based on the functionality and nature of their extensions.We first collect over 1.44 million public user reviews of our detectable extensions, which provide a unique macroscopic view of the browser extension ecosystem and enable a more precise evaluation of the discriminatory power of extensions as well as a new deanonymization vector.We also automatically categorize extensions based on the developers' descriptions and identify those that can lead to the inference of personal data (religion, medical issues, etc.).Overall, our research sheds light on previously unexplored dimensions of the privacy threats of extension fingerprinting and highlights the need for more effective countermeasures that can prevent our attacks.
Technology for real estate education and practice: a VR technology perspective

Purpose Led by the rapid advancement of information technology in engineering, business and creative industries, the emergence of new technology such as virtual reality (VR) and its use in education and practices are clearly observed. Although widely spread in industry practices, technology-led innovation is applied rather slowly in the tertiary real estate education. This paper examines the integrative effect of VR technology in a real estate course. Design/methodology/approach This study adopts a case study approach. Using an experimental course design and delivery in a business school real estate programme from Hong Kong, this paper shows the design, development and implementation of an innovative teaching model with VR being integrated. A survey was conducted to gain feedback information from participating students towards teaching innovation. It identifies the role and values of adopting VR technology in real estate education as pedagogical tool. Findings A new teaching model integrated with VR technology to deliver a real estate course has demonstrated its ability and potential to assist the development and enhancement of student's sense of value and place, as well as improving communicative efficiency of property investigation and the analysis of trade process. Findings from the study have implications for future global real estate industry practice and education. Originality/value The critical role of information technology to revolutionise the global economy and its real estate sector is apparent. Few studies have inquired about attempts and experience of integrating VR technology in real estate education towards direct link to industry practice. This paper is a major attempt to bring attention to this important concern.
This Dataset Does Not Exist: Training Models from Generated Images

Current generative networks are increasingly proficient in generating high-resolution realistic images. These generative networks, especially the conditional ones, can potentially become a great tool for providing new image datasets. This naturally brings the question: Can we train a classifier only on the generated data? This potential availability of nearly unlimited amounts of training data challenges standard practices for training machine learning models, which have been crafted across the years for limited and fixed size datasets. In this work we investigate this question and its related challenges. We identify ways to improve significantly the performance over naive training on randomly generated images with regular heuristics. We propose three standalone techniques that can be applied at different stages of the pipeline, i.e., data generation, training on generated data, and deploying on real data. We evaluate our proposed approaches on a subset of the ImageNet dataset and show encouraging results compared to classifiers trained on real images.
Overview of deep-learning based methods for salient object detection in videos

Video salient object detection is a challenging and important problem in computer vision domain. In recent years, deep-learning based methods have contributed to significant improvements in this domain. This paper provides an overview of recent developments in this domain and compares the corresponding methods up to date, including 1) Classification of the state-of-the-art methods and their frameworks; 2) summary of the benchmark datasets and commonly used evaluation metrics; 3) experimental comparison of the performances of the state-of-the-art methods; 4) suggestions of some promising future works for unsolved challenges.
Testing and Evaluation System for Cloud Computing Information Security Products

Due to the lack of professional testing and evaluation system for cloud computing information security products, the basic security of cloud computing information security products cannot be guaranteed. The establishment of a test and evaluation system of cloud computing information security products is provided, and the system is used in the actual product testing, to further promote the development of cloud computing and information security.
Banner: A Cost-Sensitive Contextualized Model for Bangla Named Entity Recognition

Named Entity Recognition (NER) is a task in Natural Language Processing (NLP) that aims to classify words into a predetermined list of Named Entities (NE). Many architectures have produced good results on high resourced languages like English and Chinese. However, the NER task has not yet achieved much progress for Bangla, a low resource Language. In this paper, we perform the NER task on Bangla Language using Word2Vec and contextual Bidirectional Encoder Representations from Transformers (BERT) embeddings. We propose multiple BERT-based deep learning models that use the contextualized embedding from BERT as inputs and a simple statistical approach for class weight cost sensitive learning. The modified cost-sensitive loss function was used to address the class imbalance of the data. In our modified cost-sensitive loss function, we penalize the dominant classes by taking the ratio concerning the maximum sample in a class instead of the whole dataset. This penalty is made so that the learner learns slowly for the dominant class. In addition, we experiment by adding a Conditional Random Field (CRF) layer and incorporating Focal Loss to the training process. We found the best F1 Macro score to be 65.96%, F1 Micro score of 90.64%, and F1 Message Understanding Coreference (MUC) score of 72.04%, which were calculated at Named Entity level. Our experimental results demonstrate that one of the proposed models, which jointly optimizes for the CRF loss and class weighted cost-sensitive loss according to our proposed statistical approach, achieve an improvement of over 8% F1 MUC score on a recently introduced Bangla NER dataset when compared to previously published work.
Stacked Residual Recurrent Neural Networks With Cross-Layer Attention for Text Classification

Text classification is a fundamental task in natural language processing and is essential for many tasks like sentiment analysis and question classification etc. As we all know, different NLP tasks require different linguistic features. Tasks such as text classification requires more semantic features than other tasks such as dependency parsing requiring more syntactic features. Most existing methods focus on improving performance by mixing and calibrating features, without distinguishing the types of features and corresponding effects. In this paper, we propose a stacked residual recurrent neural networks with cross-layer attention model to filter more semantic features for text classification, which named SRCLA. Firstly, we build a stacked network structure to filter different types of linguistic features, and then propose a novel cross-layer attention mechanism that exploits higher-level features to supervise the lower-level features to refine the filtering process. Based on this, more semantic features can be selected for text classification. We conduct experiments on eight text classification tasks, including sentiment analysis, question classification and subjectivity classification and compare with a broad range of baselines. Experimental results show that the proposed approaches achieve the state-of-the-art results on 5 out of 8 tasks.
De-Signing Hamiltonians for Quantum Adiabatic Optimization

Quantum fluctuations driven by non-stoquastic Hamiltonians have been conjectured to be an important and perhaps essential missing ingredient for achieving a quantum advantage with adiabatic optimization. We introduce a transformation that maps every non-stoquastic adiabatic path ending in a classical Hamiltonian to a corresponding stoquastic adiabatic path by appropriately adjusting the phase of each matrix entry in the computational basis. We compare the spectral gaps of these adiabatic paths and find both theoretically and numerically that the paths based on non-stoquastic Hamiltonians have generically smaller spectral gaps between the ground and first excited states, suggesting they are less useful than stoquastic Hamiltonians for quantum adiabatic optimization. These results apply to any adiabatic algorithm which interpolates to a final Hamiltonian that is diagonal in the computational basis.
A Blockchain and Smart Contract-Based Framework to Inrease Traceability of Built Assets

The UK Government's review of Building Regulations and Fire Safety 2018 identified a series of failings in the construction sector including ambiguous and inconsistent regulations and standards; lack of clarity of roles and responsibilities; lack of enforcement of regulations and standards; poor product testing, labelling and marketing; inconsistent competency across the sector; and failure to address building occupants' concerns around health and safety.Traceability and the development of a digital record to provide a golden thread of information were two recommendations of the report.To address these recommendations, this paper considered the extent to which the integration of Building Information Modelling (BIM), distributed ledger technology (DLT), the Internet of Things (IoT) and smart contracts in the form of a decentralised autonomous organisation (DAO) can support enhanced traceability and the population and management of a digital record during the building maintenance phase as part of a solution to provide a platform for the digital record.A framework is proposed to bring these technological systems together creating an ecosystem that encourages digitalisation and provides traceability, better information management and compliance with regulations and standards.Further work will involve testing the framework by simulating a range of maintenance events.
A Game Theoretic Framework for Model Based Reinforcement Learning

Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data. However, designing stable and efficient MBRL algorithms using rich function approximators have remained challenging. To help expose the practical challenges in MBRL and simplify algorithm design from the lens of abstraction, we develop a new framework that casts MBRL as a game between: (1) a policy player, which attempts to maximize rewards under the learned model; (2) a model player, which attempts to fit the real-world data collected by the policy player. For algorithm development, we construct a Stackelberg game between the two players, and show that it can be solved with approximate bi-level optimization. This gives rise to two natural families of algorithms for MBRL based on which player is chosen as the leader in the Stackelberg game. Together, they encapsulate, unify, and generalize many previous MBRL algorithms. Furthermore, our framework is consistent with and provides a clear basis for heuristics known to be important in practice from prior works. Finally, through experiments we validate that our proposed algorithms are highly sample efficient, match the asymptotic performance of model-free policy gradient, and scale gracefully to high-dimensional tasks like dexterous hand manipulation. Additional details and code can be obtained from the project page at https://sites.google.com/view/mbrl-game
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

We propose a meta-learning approach that learns from multiple tasks in a transductive setting, by leveraging the unlabeled query set in addition to the support set to generate a more powerful model for each task. To develop our framework, we revisit the empirical Bayes formulation for multi-task learning. The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task. We derive a novel amortized variational inference that couples all the variational posteriors via a meta-model, which consists of a synthetic gradient network and an initialization network. Each variational posterior is derived from synthetic gradient descent to approximate the true posterior on the query set, although where we do not have access to the true gradient. Our results on the Mini-ImageNet and CIFAR-FS benchmarks for episodic few-shot classification outperform previous state-of-the-art methods. Besides, we conduct two zero-shot learning experiments to further explore the potential of the synthetic gradient.