Reviewerly/exHarmony huggingface dataset:
Using Amazon Alexa APIs as a Source of Digital Evidence

With the release of Amazon Alexa and the first Amazon Echo device, the company revolutionised the smart home. It allowed their users to communicate with, and control, their smart home ecosystem purely using voice commands. However, this also means that Amazon processes and stores a large amount of personal data about their users, as these devices are always present and always listening in peoples' private homes. That makes this data a valuable source of evidence for investigators performing digital forensics. The Alexa Voice Service uses a series of APIs for communication between clients and the Amazon cloud. These APIs return a wide range of data related to the functionality of the device used.The first goal of this research was to clarify exactly what kind of information about the user is stored and accessible through these APIs. To do this, a combination of literature review and exploratory analysis was used to establish a list of all relevant APIs. Then, possible artefacts and conclusions to be drawn from their responses were identified and presented. Lastly, the perspective of the users was taken, and options for improving their privacy were reviewed. Specifically, the history of interaction between the user and Alexa is available through multiple APIs, and there are several options to delete it. It was determined that these options have different behaviours and that most of them do not remove all data related to user interaction.
DPCrowd: Privacy-Preserving and Communication-Efficient Decentralized Statistical Estimation for Real-Time Crowdsourced Data

In Internet-of-Things (IoT)-driven smart-world systems, real-time crowdsourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowdsourcing participants. For mitigating the privacy concern, while the traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multidimensional crowdsourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowdsourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multidimensional infinite crowd-data streams. Extensive experiments on several data sets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled data when labeled data is scarce, the existing SSL algorithms typically assume that training class distributions are balanced. However, these SSL algorithms trained under imbalanced class distributions can severely suffer when generalizing to a balanced testing criterion, since they utilize biased pseudo-labels of unlabeled data toward majority classes. To alleviate this issue, we formulate a convex optimization problem to softly refine the pseudo-labels generated from the biased model, and develop a simple algorithm, named Distribution Aligning Refinery of Pseudo-label (DARP) that solves it provably and efficiently. Under various class-imbalanced semi-supervised scenarios, we demonstrate the effectiveness of DARP and its compatibility with state-of-the-art SSL schemes.
Criteria for single photon sources with variable nonclassicality threshold

Single photon sources are necessary for optical quantum technology. The nonclassicality of emitted single photons manifests itself in diverse experiments and applications. The applications already require different nonclassical aspects of single photons, however, a suitable hierarchy of criteria is missing. We propose variable experimental tests, based on adjustable linear optical networks and single photon detectors, giving a hierarchy of such nonclassicality criteria. The hierarchy goes beyond Hanbury - Brown - Twiss test of photon antibunching and allows us to compare faithfully quality and performance of single-photon sources via their nonclassical properties.
Selective attention to question-relevant text information precedes high-quality summaries: Evidence from eye movements

Comprehension and summarizing are closely related. As more strategic and selective processing during reading should be reflected in higher quality of summaries, the aim of this study was to use eye movement patterns to analyze how readers who produce good quality summaries process texts. 40 undergraduate students were instructed to read six expository texts in order to respond a causal question introduced in the end of the first paragraph. After reading, participants produced an oral summary of the text. Based on the quality of the summaries, participants were divided into three groups: High, Medium and Low Quality Summaries. The results revealed that readers who produced High Quality Summaries made significantly more and longer fixations and regressions in the question-relevant parts of texts when compared to the other two summary groups. These results suggest that the summary task performance could be a good predictor of the reading strategies utilized during reading.
Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments

Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecturers (particular when it comes to repeatedly delivered subjects). In this paper, we compare the performance of traditional machine learning algorithms and two deep learning methods in extracting topic keywords from student comments posted in subject forums. For this purpose, we collected student comment data from a period of two years, manually tagging part of the raw data for our experiments. Based on this dataset, we comprehensively compared the five typical algorithms of naïve Bayes, logistic regression, support vector machine, convolutional neural networks, and Long Short-Term Memory with Attention (Att-LSTM). The performances were measured by the four evaluation metrics. We further examined the keywords by visualization. From the results of our experiment and visualization, we conclude that the Att-LSTM method is the best approach for topic keyword extraction from student comments. Further, the results from the algorithms and visualization are symmetry, to some degree. In particular, the extracted topics from the comments posted at the same stages of different teaching sessions are, almost, reflection symmetry.
DIGITAL SKILLS DEVELOPMENT AND ICT TOOLS FOR FINAL QUALIFICATION ASSESSMENT: SURVEY STUDY FOR STUDENTS AND STAFF OF EUROPEAN AND ORIENTAL PHILOLOGY PROGRAMS

The global pandemic and subsequent quarantine measures and restrictions have posed an array of challenges to the structure and procedure of university summative assessment process. Qualification assessment for Foreign Languages major programs in particular is a strict regimen process that involves different stages (oral and written exams, final project viva, internal and external review). Factors of societal change, that provide the backdrop for skillset critical transformation, crucial for the COVID-19 emergency educational framework are considered. The study premise is based on identification of various competency principles, derivative of 21st century skills for university staff members and projected digital literacy requirements. A correspondence between 21st century skills framework, Competences 2020 framework and the newly devised Global Skills framework has been devised. The study objective is to critically review the applied case and best practices of Borys Grinchenko Kyiv University Digital Final Qualification Assessment for students of European (French, Italian, Spanish, English, German) and Oriental (Mandarin Chinese, Japanese) Languages major programs, employed in the year 2020 due to quarantine measures. The survey and analysis of different ICT tools is used to translate real life qualification assessment practices into online blended format. The investigation also seeks to identify various groups of applied digital skills and collaboration skills, utilized through qualification assessment process by all parties (students, faculty and referees). It is determined how in the situation of the COVID-19 pandemic lockdown all elements of the Final Qualification Assessment at Borys Grinchenko Kyiv University for European and Oriental Languages programs have been relegated to the digital, remote or blended format with the use of ICT tools. The qualification assessment regimen for foreign languages major programs was adapted to digital format as a framework (a legal procedure that results in the degree confirmation of a student), the string of consecutive communicative activities according to the legal procedure, the "ritual" scenario. Comparative results of the Final Qualification Assessment digital format adaptation for different groups of foreign languages programs (European and Oriental) are devised
An adaptive prediction method based on data stream mining for future driving cycle of vehicle

Due to complex and changeable driving cycles in urban roads, it is a challenging task for most of the current control strategies utilized in vehicles to adapt to the driving environment. At the same time, hardware requirements for storing and processing a massive amount of streaming data are increasing, which lead to excessive accumulated errors and high computational cost. To deal with this problem, an innovative prediction method, which is based on Markov chain and data stream mining, is proposed to predict the future driving cycle of vehicles. State transition probability matrix is updated in real time with data stream mining technology, and every time a new record arrives, the expired record is replaced by the new arrived one in the memory, and both state division and the sizes of the sliding window can be adjusted adaptively based on prediction accuracy for the changing driving cycles. The results show that the proposed method is more suitable for predicting changing driving cycles, which is able to maintain better prediction accuracy than the traditional method. In addition, based on the proposed method, the memory space utilized for storing temporary records were saved largely, and the calculation resource required was reduce.
World age in Julia: optimizing method dispatch in the presence of eval

Dynamic programming languages face semantic and performance challenges in the presence of features, such as eval, that can inject new code into a running program. The Julia programming language introduces the novel concept of world age to insulate optimized code from one of the most disruptive side-effects of eval: changes to the definition of an existing function. This paper provides the first formal semantics of world age in a core calculus named juliette, and shows how world age enables compiler optimizations, such as inlining, in the presence of eval. While Julia also provides programmers with the means to bypass world age, we found that this mechanism is not used extensively: a static analysis of over 4,000 registered Julia packages shows that only 4-9% of packages bypass world age. This suggests that Julia's semantics aligns with programmer expectations.
Wavelets in the Deep Learning Era

Sparsity based methods, such as wavelets, have been state-of-the-art for more than 20 years for inverse problems before being overtaken by neural networks. In particular, U-nets have proven to be extremely effective. Their main ingredients are a highly non-linear processing, a massive learning made possible by the flourishing of optimization algorithms with the power of computers (GPU) and the use of large available data sets for training. While the many stages of non-linearity are intrinsic to deep learning, the usage of learning with training data could also be exploited by sparsity based approaches. The aim of our study is to push the limits of sparsity with learning, and comparing the results with U-nets. We present a new network architecture, which conserves the properties of sparsity based methods such as exact reconstruction and good generalization properties, while fostering the power of neural networks for learning and fast calculation. We evaluate the model on image denoising tasks and show it is competitive with learning-based models.
A Hardware in the Loop Benchmark Suite to Evaluate NIST LWC Ciphers on Microcontrollers

The National Institute of Standards and Technology (NIST) started the standardization process for lightweight cryptography algorithms in 2018. By the end of the first round, 32 submissions have been selected as 2nd round candidates. NIST allowed designers of 2nd round submissions to provide small updates on both their specifications and implementation packages. In this work, we introduce a benchmarking framework for evaluating the performance of NIST Lightweight Cryptography (LWC) candidates on embedded platforms. We show the features and application of the framework and explain its design rationale. Moreover, we provide information on how we aim to present up-to-date performance figures throughout the NIST LWC competition. In this paper, we present an excerpt of our software benchmarking results regarding speed and memory requirements of selected ciphers. All up-to-date results, including benchmarking different test cases for multiple variants of each 2nd round algorithm on five different microcontrollers, are periodically published to a public website. While initially only the reference implementations were available, the ability of automatically testing the performance of the candidate algorithms on multiple platforms becomes especially relevant as more optimized implementations are developed. Finally, we show how the framework can be extended in different directions: support for more target platforms can be easily added, different kinds of algorithms can be tested, and other test metrics can be acquired. The focus of this paper should rather lay on the framework design and testing methodology than on the current results, especially for reference code.
Continuous-variable entanglement in a two-mode lossy cavity: An analytic solution

Continuous-variable (CV) entanglement is a valuable resource in the field of quantum information. One source of CV entanglement is the correlations between the position and momentum of photons in a two-mode squeezed state of light. In this paper, we theoretically study the generation of squeezed states, via spontaneous parametric downconversion (SPDC), inside a two-mode lossy cavity that is pumped with a classical optical pulse. The dynamics of the density operator in the cavity is modelled using the Lindblad master equation, and we show that the exact solution to this model is the density operator for a two-mode squeezed thermal state, with a time-dependent squeezing amplitude and average thermal photon number for each mode. We derive an expression for the maximum entanglement inside the cavity that depends crucially on the difference in the losses between the two modes. We apply our exact solution to the important example of a microring resonator that is pumped with a Gaussian pulse. The expressions that we derive will help researchers optimize CV entanglement in lossy cavities.
INFILTRATION–EXFILTRATION SYSTEMS DESIGN UNDER HYDROLOGICAL UNCERTAINTY

The current scenario of urban drainage management encourages the use of sustainable urban drainage systems (SUDSs) acting on runoff volume, peak flow and quality standards.Infiltration-exfiltration systems (IES) are part of SUDSs: they are composed of a high permeability surface and a structure that functions as a reservoir; depending on site characteristics, stormwater can be infiltrated on subsoil, drained or a combination of both.IES design consists basically of sizing the reservoir layer depth according to rainfall, soil and drainage characteristics considering a maximum drainage time between 24 and 72 hours.If long records of rainfall data are available, continuous simulations are performed, otherwise the so called "design event" method is used.It considers a single rainfall event, neglecting the possibility that the storage capacity can be partially filled from previous rainfalls.This paper proposes an analytical probabilistic approach for IES design, combining the simplicity of "design event" methods and the statistical reliability of continuous simulations.It considers the possibility of pre-filling of the reservoir layer from more than one previous event, a key aspect for the correct design of low release structures as IES.The approach has been tested applying proposed equations to a case study, in Milan, Italy, and comparing the results with those from the continuous simulation of recorded data.
Improving Neural Network With Uniform Sparse Connectivity

Neural network forms the foundation of deep learning and numerous AI applications. Classical neural networks are fully connected, expensive to train and prone to overfitting. Sparse networks tend to have convoluted structure search, suboptimal performance and limited usage. We proposed the novel uniform sparse network (USN) with even and sparse connectivity within each layer. USN has one striking property that its performance is independent of the substantial topology variation and enormous model space, thus offers a search-free solution to all above mentioned issues of neural networks. USN consistently and substantially outperforms the state-of-the-art sparse network models in prediction accuracy, speed and robustness. It even achieves higher prediction accuracy than the fully connected network with only 0.55% parameters and 1/4 computing time and resources. Importantly, USN is conceptually simple as a natural generalization of fully connected network with multiple improvements in accuracy, robustness and scalability. USN can replace the latter in a range of applications, data types and deep learning architectures. We have made USN open source at https://github.com/datapplab/sparsenet.
Lack of Motivation Factors Creating Poor Academic Performance in the Context of Graduate English Department Students

At the graduate level, students’ poor performance cuts across almost all the compulsory subjects in which English is inclusive. Poor academic performance of students is one problem impeding the smooth actualization of the educational purpose, which is to prepare an individual for mentally serving both himself and society. Therefore, the primary purpose of this research is to discover the internal factors, such as lack of motivation factors contributing to the poor academic performance of graduate students of the English Department. In this research, researchers selected three participants and utilized an interview as the method to collect the data, and interview guidelines as the instrument. The authors analysed the data qualitatively and explained the findings descriptively. Several things will affect students’ academic performance, especially those related to motivation. It starts from within the students themselves, from the family environment, and even lecturers greatly affect student motivation in improving academic performance. It should not only focus on the mistakes made by students, as there must be a cause behind it all. In a nutshell, the results of this study revealed that one aspect of internal factors causing poor academic performance was the lack of motivation derived from two sizeable directions, family and teacher.
Towards Real-Time Hidden Speaker Recognition by Means of Fully Homomorphic Encryption

Securing Neural Network (NN) computations through the use of Fully Homomorphic Encryption (FHE) is the subject of a growing interest in both communities. Among different possible approaches to that topic, our work focuses on applying FHE to hide the model of a neural network-based system in the case of a plain input. In this paper, using the TFHE homomorphic encryption scheme, we propose an efficient method for an $$\mathsf {argmin}$$ computation on an arbitrary number of encrypted inputs and an asymptotically faster - though levelled - equivalent scheme. Using these schemes and a unifying framework for LWE-based homomorphic encryption schemes (Chimera), we implement a practically efficient, homomorphic speaker recognition system using the embedding-based neural net system VGGVox. This work can be applied to all other similar Euclidean embedding-based recognition systems (e.g. Google’s FaceNet). While maintaining the best-in-class classification rate of the VGGVox system, we demonstrate a speaker-recognition system that can classify a speech sample as coming from one out of 50 hidden speaker models in less than one minute.
Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize instances of unseen classes, for which no visual instance is available during training, by learning multimodal relations between samples from seen classes and corresponding class semantic representations. These class representations usually consist of either attributes, which do not scale well to large datasets, or word embeddings, which lead to poorer performance. A good trade-off could be to employ short sentences in natural language as class descriptions. We explore different solutions to use such short descriptions in a ZSL setting and show that while simple methods cannot achieve very good results with sentences alone, a combination of usual word embeddings and sentences can significantly outperform current state-of-the-art.
Collaborative Filtering Item Recommendation Methods based on Matrix Factorization and Clustering Approaches

This paper studies various collaborative filtering item recommendation methods based on matrix factorization and clustering approaches. We develop six methods that are modified based on three matrix factorization approaches, i.e., Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), and Principal Component Analysis (PCA); and two clustering techniques, i.e., K-Means and Fuzzy C-Means. The framework of method development consists of three main phases: the matrix factorization for generating the latent factors; the users clustering for grouping similar users; and the user-based collaborative filtering for generating the item recommendation. The recommendation performances are evaluated in terms of Fl-Score and Normalized Discounted Cumulative Gain (NDCG) metrics, at various top-N. Using the Movie Lens rating dataset, experiment results show that the combination of PCA and K-Means outperforms the other five methods, where the global averages of outperformance are 82.05% in terms of Fl-Score and 52.10% in terms of NDCG. It is also worthwhile to note that the combination of K-Means clustering with any matrix factorization techniques is superior compared to that of the Fuzzy C-Means. Additionally, the advantage of implementing the matrix factorization approach depends on which clustering technique used in the method.