Reviewerly/exHarmony huggingface dataset:
Adversarially Robust Streaming via Dense--Sparse Trade-offs

A streaming algorithm is adversarially robust if it is guaranteed to perform correctly even in the presence of an adaptive adversary. Recently, several sophisticated frameworks for robustification of classical streaming algorithms have been developed. One of the main open questions in this area is whether efficient adversarially robust algorithms exist for moment estimation problems under the turnstile streaming model, where both insertions and deletions are allowed. So far, the best known space complexity for streams of length $m$, achieved using differential privacy (DP) based techniques, is of order $\tilde{O}(m^{1/2})$ for computing a constant-factor approximation with high constant probability. In this work, we propose a new simple approach to tracking moments by alternating between two different regimes: a sparse regime, in which we can explicitly maintain the current frequency vector and use standard sparse recovery techniques, and a dense regime, in which we make use of existing DP-based robustification frameworks. The results obtained using our technique break the previous $m^{1/2}$ barrier for any fixed $p$. More specifically, our space complexity for $F_2$-estimation is $\tilde{O}(m^{2/5})$ and for $F_0$-estimation, i.e., counting the number of distinct elements, it is $\tilde O(m^{1/3})$. All existing robustness frameworks have their space complexity depend multiplicatively on a parameter $\lambda$ called the \emph{flip number} of the streaming problem, where $\lambda = m$ in turnstile moment estimation. The best known dependence in these frameworks (for constant factor approximation) is of order $\tilde{O}(\lambda^{1/2})$, and it is known to be tight for certain problems. Again, our approach breaks this barrier, achieving a dependence of order $\tilde{O}(\lambda^{1/2 - c(p)})$ for $F_p$-estimation, where $c(p) > 0$ depends only on $p$.
Picture-vocab: Self-made picture dictionary to improve pupils’ vocabulary retention in Malaysia

&lt;span lang="EN-US"&gt;This project was conducted to investigate whether the use of pictures able to improve pupils’ vocabulary retention. There were 36 pupils from an urban school located in Malaysia who are in year 4 involved as participants. The pupils were of average proficiency level of the English Language. A pre-test and post-test were conducted in order to identify the effectiveness of the intervention. An interview was carried out with 10 pupils in the post intervention for triangulation purpose. The scores from the pre-test and post-test were compared to measure the effectiveness of the intervention. Whereas, the analysis of the interview was procured on pupils’ perception towards the intervention. The findings indicate that pupils are able to remember the vocabularies taught for a longer period of time when the words are associated with pictures. Findings also indicate the use of the pictures in improving their vocabulary retention.&lt;/span&gt;
Communication-Optimal Parallel Standard and Karatsuba Integer Multiplication in the Distributed Memory Model

We present COPSIM a parallel implementation of standard integer multiplication for the distributed memory setting, and COPK a parallel implementation of Karatsuba's fast integer multiplication algorithm for a distributed memory setting. When using $\mathcal{P}$ processors, each equipped with a local non-shared memory, to compute the product of tho $n$-digits integer numbers, under mild conditions, our algorithms achieve optimal speedup of the computational time. That is, $\mathcal{O}\left(n^2/\mathcal{P}\right)$ for COPSIM, and $\mathcal{O}\left(n^{\log_2 3}/\mathcal{P}\right)$ for COPK. The total amount of memory required across the processors is $\mathcal{O}\left(n\right)$, that is, within a constant factor of the minimum space required to store the input values. We rigorously analyze the Input/Output (I/O) cost of the proposed algorithms. We show that their bandwidth cost (i.e., the number of memory words sent or received by at least one processors) matches asymptotically corresponding known I/O lower bounds, and their latency (i.e., the number of messages sent or received in the algorithm's critical execution path) is asymptotically within a multiplicative factor $\mathcal{O}\left(\log^2_2 \mathcal{P}\right)$ of the corresponding known I/O lower bounds. Hence, our algorithms are asymptotically optimal with respect to the bandwidth cost and almost asymptotically optimal with respect to the latency cost.
CENTRAL BANK DIGITAL CURRENCY UNDER THE STATE THEORY OF MONEY: A PRELIMINARY LEGAL ANALYSIS

Innovations in digital payments have triggered many central banks to apprehend and consider central bank digital currency (CBDC). CBDC is believed to be the next milestone in the evolution of money because many studies have shown the significant advantages of using government-issued digital currency. However, to function as money, CBDC must fulfil the fundamental role of money, among others, contended by the state theory of money. This paper addresses the applicability of the state theory of money to CBDC as digital money, which is prefaced by the discussion on money as legal tender and the theory of sovereign power over money. After analysing these theories, this paper offers a preliminary legal analysis of CBDC, mainly from the perspective of Indonesian law. This paper concludes that the concept of CBDC as money and legal tender may fulfil the notion of money under the state theory of money and can serve as legal tender. Applying this theory to CBDC and assuming CBDC is used as legal tender, the State should create a legal framework to regulate CBDC as a valid medium of exchange and legal tender. However, it is also acknowledged that various designs of CBDC must be supported by different legal environments. Furthermore, this paper recommends the preparation of an Indonesian legal ecosystem for CBDC, consisting of a solid regulatory framework and clear legal relationships among relevant parties, that are needed to ensure the legality of the issuance, distribution and transference of CBDC once the design of CBDC is determined. Furthermore, to accommodate the use of CBDC, a thorough assessment of the relevant Indonesian laws should be undertaken relating, among other factors, to the central bank, money, currency, and technology.
Cross-Domain Action Recognition via Prototypical Graph Alignment

Compared with the well-explored cross-domain image recognition, cross-domain action recognition is a more challenging task because not only spatial but also temporal domain gaps exist across domains. Previous works attempt to bridge the temporal domain gap by aligning the domain-related key segments of videos from source and target domains. However, such practice overlooks the heterogeneous temporal domain gaps among different categories and presents temporal alignment strategies in a class-irrelevant manner. To address this issue, we propose to achieve class-wise temporal alignment for cross-domain action recognition via prototypical graph alignment (PGA). Concretely, we generate segment-level prototypes for the classes of both domains to capture per-class temporal dynamics. Furthermore, intra-domain and inter-domain prototypical graphs are established to mine the temporal relationships between each input video and its corresponding intra-domain and inter-domain prototypes. In this way, a discriminative and domain adaptive video representation is obtained by holistically reasoning cross-domain temporal dynamics. To class-wisely align the cross-domain video representations, each action category is equipped with a customized class-specific domain discriminator for temporal alignment via adversarial learning. Extensive experiments on three benchmarks show that PGA yeilds state-of-the-art performance on the task of cross-domain action recognition.
A Column Streaming-Based Convolution Engine and Mapping Algorithm for CNN-based Edge AI accelerators

Edge AI accelerators have been emerging as a solution for near customers' applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications not only require meeting performance targets but also meeting strict area and power constraints due to their portable mobility feature and limited power sources. As a result, a column streaming-based convolution engine has been proposed in this paper that includes column sets of processing elements design for flexibility in terms of the applicability for different CNN algorithms in edge AI accelerators. Comparing to a commercialized CNN accelerator, the key results reveal that the column streaming-based convolution engine requires similar execution cycles for processing a 227 x 227 feature map with avoiding zero-padding penalties.
Thinging the Use Case Model

Use cases as textual visual modeling techniques have become a key construct and the foundation of the most popular de facto standard technique for performing software requirements analysis and specification. This paper describes use cases in terms of a recently proposed model, the thinging machine (TM) model. Such a link to TM strengthens the notion of use cases and clarifies related concepts. For example, the concept of events is utilized in an unconstrained way in use cases, and TM may contribute in this direction. Several selected use cases are remodeled using TM. This study provides many insights. For example, one result shows that use cases are an activation apparatus of mega-triggering (high-level events) wherein a group of submachines (processes) are actuated.
From the Lab to the Stage: Practical Considerations on Designing Performances with Immersive Virtual Musical Instruments

Abstract Immersive virtual musical instruments (IVMIs) lie at the intersection between music technology and virtual reality. Being both digital musical instruments (DMIs) and elements of virtual environments (VEs), IVMIs have the potential to transport the musician into a world of imagination and unprecedented musical expression. But when the final aim is to perform live on stage, the employment of these technologies is anything but straightforward, for sharing the virtual musical experience with the audience gets quite arduous. In this chapter, we assess in detail the several technical and conceptual challenges linked to the composition of IVMI performances on stage, i.e., their scenography , providing a new critical perspective on IVMI performance and design. We first propose a set of dimensions meant to analyse IVMI scenographies, as well as to evaluate their compatibility with different instrument metaphors and performance rationales. Such dimensions are built from the specifics and constraints of DMIs and VEs; they include the level of immersion of musicians and spectators and provide an insight into the interaction techniques afforded by 3D user interfaces in the context of musical expression. We then analyse a number of existing IVMIs and stage setups, and finally suggest new ones, with the aim to facilitate the design of future immersive performances.
A Multilingual Encoding Method for Text Classification and Dialect Identification Using Convolutional Neural Network

This thesis presents a language-independent text classification model by introduced two new encoding methods "BUNOW" and "BUNOC" used for feeding the raw text data into a new CNN spatial architecture with vertical and horizontal convolutional process instead of commonly used methods like one hot vector or word representation (i.e. word2vec) with temporal CNN architecture. The proposed model can be classified as hybrid word-character model in its work methodology because it consumes less memory space by using a fewer neural network parameters as in character level representation, in addition to providing much faster computations with fewer network layers depth, as in word level representation. A promising result achieved compared to state of art models in two different morphological benchmarked dataset one for Arabic language and one for English language.
Majority Voting with Bidirectional Pre-translation For Bitext Retrieval

Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called "pseudo-parallel" sentences from paired documents in two languages. In this paper, we outline some problems with current methods, propose computationally economical solutions to those problems, and demonstrate success with novel methods on the Tatoeba similarity search benchmark and on a downstream task, namely NMT. We uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining approach, and echo problems with the oft-used BUCC dataset that have been observed by others. We make the code and data used for our experiments publicly available.
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks

The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate representations for quantization with minimal accuracy loss while significantly reducing the memory footprint and compute intensity of the DNN. This paper utilizes knowledge distillation through teacher-student paradigm (Hinton et al., 2015) in a novel setting that exploits the feature extraction capability of DNNs for higher-accuracy quantization. As such, our algorithm logically divides a pretrained full-precision DNN to multiple sections, each of which exposes intermediate features to train a team of students independently in the quantized domain. This divide and conquer strategy, in fact, makes the training of each student section possible in isolation while all these independently trained sections are later stitched together to form the equivalent fully quantized network. Our algorithm is a sectional approach towards knowledge distillation and is not treating the intermediate representation as a hint for pretraining before one knowledge distillation pass over the entire network (Romero et al., 2015). Experiments on various DNNs (AlexNet, LeNet, MobileNet, ResNet-18, ResNet-20, SVHN and VGG-11) show that, this approach -- called DCQ (Divide and Conquer Quantization) -- on average, improves the performance of a state-of-the-art quantized training technique, DoReFa-Net (Zhou et al., 2016) by 21.6% and 9.3% for binary and ternary quantization, respectively. Additionally, we show that incorporating DCQ to existing quantized training methods leads to improved accuracies as compared to previously reported by multiple state-of-the-art quantized training methods.
Communication over Quantum Channels with Parameter Estimation

Communication over a random-parameter quantum channel when the decoder is required to reconstruct the parameter sequence is considered. We study scenarios that include either strictly-causal, causal, or non-causal channel side information (CSI) available at the encoder, and also when CSI is not available. This model can be viewed as a form of quantum metrology, and as the quantum counterpart of the classical rate-and-state channel with state estimation at the decoder. Regularized formulas for the capacity-distortion regions are derived. In the special case of measurement channels, single-letter characterizations are derived for the strictly causal and causal settings. Furthermore, in the more general case of entanglement-breaking channels, a single-letter characterization is derived when CSI is not available. As a consequence, we obtain regularized formulas for the capacity of random-parameter quantum channels with CSI, generalizing previous results by Boche et al. (2016) on classical-quantum channels.
Bayesian Aggregation

A general challenge in statistics is prediction in the presence of multiple candidate models or learning algorithms. Model aggregation tries to combine all predictive distributions from individual models, which is more stable and flexible than single model selection. In this article we describe when and how to aggregate models under the lens of Bayesian decision theory. Among two widely used methods, Bayesian model averaging (BMA) and Bayesian stacking, we compare their predictive performance, and review their theoretical optimality, probabilistic interpretation, practical implementation, and extensions in complex models.
Revisiting the Uniform Information Density Hypothesis

The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehension and linguistic acceptability as well. Further, it is unclear how uniformity in a linguistic signal -- or lack thereof -- should be measured, and over which linguistic unit, e.g., the sentence or language level, this uniformity should hold. Here we investigate these facets of the UID hypothesis using reading time and acceptability data. While our reading time results are generally consistent with previous work, they are also consistent with a weakly super-linear effect of surprisal, which would be compatible with UID's predictions. For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability. We then explore multiple operationalizations of UID, motivated by different interpretations of the original hypothesis, and analyze the scope over which the pressure towards uniformity is exerted. The explanatory power of a subset of the proposed operationalizations suggests that the strongest trend may be a regression towards a mean surprisal across the language, rather than the phrase, sentence, or document -- a finding that supports a typical interpretation of UID, namely that it is the byproduct of language users maximizing the use of a (hypothetical) communication channel.
Slicing functional aspects out of legacy applications

Aspect-oriented software development builds upon object-oriented development by offering additional abstraction boundaries that help us separate different types of requirements into so-called aspects. Given a legacy OO application, it pays to identify existing aspects to help understand the structure of the application and potentially to extract those aspects into separate software modules that can be maintained and reused individually. We present an approach to extract functional aspects using program slicing. We first characterize what we mean by functional aspect, and explore the extent to which program slicing techniques could improve over existing aspect mining and feature location methods for such aspects. We then describe the results of applying our approach to two medium-sized open source programs. Our preliminary results show that program slicing can yield well-defined functional aspects when the appropriate slicing criteria are used. Finally, we explore the effect of design quality on the candidate functional aspects.
Scalable Bayesian high-dimensional local dependence learning

In this work, we propose a scalable Bayesian procedure for learning the local dependence structure in a high-dimensional model where the variables possess a natural ordering. The ordering of variables can be indexed by time, the vicinities of spatial locations, and so on, with the natural assumption that variables far apart tend to have weak correlations. Applications of such models abound in a variety of fields such as finance, genome associations analysis and spatial modeling. We adopt a flexible framework under which each variable is dependent on its neighbors or predecessors, and the neighborhood size can vary for each variable. It is of great interest to reveal this local dependence structure by estimating the covariance or precision matrix while yielding a consistent estimate of the varying neighborhood size for each variable. The existing literature on banded covariance matrix estimation, which assumes a fixed bandwidth cannot be adapted for this general setup. We employ the modified Cholesky decomposition for the precision matrix and design a flexible prior for this model through appropriate priors on the neighborhood sizes and Cholesky factors. The posterior contraction rates of the Cholesky factor are derived which are nearly or exactly minimax optimal, and our procedure leads to consistent estimates of the neighborhood size for all the variables. Another appealing feature of our procedure is its scalability to models with large numbers of variables due to efficient posterior inference without resorting to MCMC algorithms. Numerical comparisons are carried out with competitive methods, and applications are considered for some real datasets.
From Average Embeddings To Nearest Neighbor Search

In this note, we show that one can use average embeddings, introduced recently in [Naor'20, arXiv:1905.01280], to obtain efficient algorithms for approximate nearest neighbor search. In particular, a metric $X$ embeds into $\ell_2$ on average, with distortion $D$, if, for any distribution $\mu$ on $X$, the embedding is $D$ Lipschitz and the (square of) distance does not decrease on average (wrt $\mu$). In particular existence of such an embedding (assuming it is efficient) implies a $O(D^3)$ approximate nearest neighbor search under $X$. This can be seen as a strengthening of the classic (bi-Lipschitz) embedding approach to nearest neighbor search, and is another application of data-dependent hashing paradigm.
A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections

In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (more than 24 hours) can slow decision-making. In contrast, fresh frozen (FF) processing (less than 1 hour) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images ,virtual FFPE, from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort.