Reviewerly/exHarmony huggingface dataset:
The Use of Games for Learning in Primary Schools: A Bibliometrics Analysis of The Scopus Database

Elementary school learning that is integrated with games can increase student motivation in learning so that it can have a positive impact on learning outcomes. The bibliometric review used in this research aims to analyze articles that discuss the use of games at the elementary school level published in the Scopus database indexed journal for the period 2013-2022. A total of 122 articles discussing the integration of games in primary school level learning were analyzed. The review findings show that there was a significant increase in the number of publications in 2016-2021, around 77.87% of articles were published in that period. Spain and Indonesia dominate the number of publications. Four authors with 3 publications each are influential authors. Data visualization shows that this research topic can be grouped based on the type of game, type of game, subject of study, learning approach, type of learning, subject involved in research, and a number of topics that have the potential to be integrated with other topics to obtain novelty. The findings of this study can help relevant researchers to recognize research trends in the use of games for primary school learning globally and recommend directions for further research.
USING THINK-AHEAD AND WRITE (TAW) STRATEGY TO IMPROVE STUDENTS' WRITING SKILL IN DESCRIPTIVE TEXT

According to the school-based curriculum (K-13) there are several types of texts that students study. Descriptive text is one of the texts that must be mastered by students. Descriptive text is a general text that students already know, but most students are still confused when they want to describe something. Writing is an important skill, but in reality there are still many students who have difficulty writing a sentence and making it into a perfect paragraph. This study aims to improve students' writing skills in descriptive texts by using Think-Ahead and Write (TAW) strategies in class XI SMA. The use of Think-Ahead and Write (TAW) strategy is an effort that is needed to improve students' learning abilities, especially to improve students' writing skills. Think-Ahead and Write (TAW) is a strategy that can help students understand the concept of writing to develop students' thinking skills. Using this type of descriptive qualitative research and as a sample the students of class XI SMAN 1 Batukliang were selected. Data collection in this study used the following instruments: observation, interviews and questionnaires to analyze the data in descriptive qualitative form. More specifically, this research is expected to be able to describe (1.) whether the teacher is able to implement or not teach descriptive text writing skills using the TAW strategy; (2) how teachers implement TAW strategies in learning; (3) Students' understanding in producing descriptive text using TAW strategy. This research is planned for 1 year, while the mandatory output target of this research is to publish articles in accredited national journals ranked 1-6. This research is at TKT 2. This research is expected to be a window for further studies or references to explore the potential benefits and challenges of the Think-Ahead And Write (TAW) strategy itself in learning to write descriptive texts, especially for secondary education (SMA).
A Generalization of the Stratonovich’s Value of Information and Application to Privacy-Utility Trade-off

The Stratonovich’s value of information (VoI) is quantity that measures how much inferential gain is obtained from noisy data under information leakage constraint. In this paper, we introduce a generalized VoI for a general loss function and general information leakage. Then we derive an upper bound of the generalized VoI. Moreover, for a classical loss function, we provide a achievable condition of the upper bound which is weaker than that of in previous studies. Since VoI can be viewed as a formulation of a privacy-utility trade-off (PUT) problem, we provide an interpretation of the achievable condition in the PUT context.
On how to avoid exacerbating spurious correlations when models are overparameterized

Overparameterized learning architectures fail to generalize well in the presence of data imbalance even when combined with traditional techniques for mitigating imbalances. This paper focuses on imbalanced classification datasets, in which a small subset of the population—a minority—may contain features that correlate spuriously with the class label. For a parametric family of cross-entropy loss modifications and a representative Gaussian mixture model, we derive non-asymptotic generalization bounds on the worst-group error that shed light on the role of different hyper-parameters. Specifically, we prove that, when appropriately tuned, the recently proposed VS-loss learns a model that is fair towards minorities even when spurious features are strong. On the other hand, alternative heuristics, such as the weighted CE and the LA-loss, can fail dramatically. Compared to previous works, our bounds hold for more general models, they are non-asymptotic, and, they apply even at scenarios of extreme imbalance.
Private Balance-Checking on Blockchain Accounts Using Private Integer Addition

A transaction record in a sharded blockchain can be represented as a two-dimensional array of integers with row-index associated to an account, column-index to a shard and the entry to the transaction amount. In a blockchain-based cryptocurrency system with coded sharding, a transaction record of a given epoch of time is encoded using a maximum-distance-separable code considering the entries as finite-field symbols. Each column of the resultant coded array is then stored in a server. In this paper, we propose a privacy-preserving multi-round protocol that allows a remote client to retrieve from a coded blockchain system the sum of transaction amounts belonging to two different epochs of time, but to the same ac-count. At the core of the protocol lies an algorithm for a remote client to privately compute a non-linear function referred to as integer addition of two finite-field symbols representing integer numbers, in the presence of curious-but-honest adversaries. Applying it to balance-checking in a cryptocurrency system, the protocol guarantees information-theoretic privacy on account number and shard number thereby ensuring perfect user anonymity, and also maintains confidentiality of half of the input bits on average. The protocol turns out to be a useful primitive for balance-checking in lightweight clients of a PolyShard-ed blockchain.
Equivalence of Quantum Single Insertion and Single Deletion Error-Correctabilities, and Construction of Codes and Decoders

This article contributes to an unsolved quantum information problem: the equivalence between insertion error-correctability and deletion error-correctability. The solution in Shibayama and Ouyang’s sense is provided in the single-error under the assumption of the purity of quantum codeword states On the other hand, a class of codes that can correct both single deletion and single insertion is provided.
Robust Locally Positioning Sequences and Codes: Capacity, Constructions and Applications

An (n, d, b, k) <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> -robust locally positioning (RLP) sequence s is a q-ary sequence of length n where each subset of b consecutive windows of length k in s is a q-ary code of length k with minimum Hamming distance d. A set of these sequences is called an (n, d, b, k) <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> -robust locally positioning code. The RLP sequence is a generalization of a locally constrained de Bruijn sequence and a robust positioning sequence which have been studied recently owing to their various applications. In this work, we study the RLP sequences and codes with motivation from both practical and theoretical points of view. Firstly, these RLP sequences and codes are useful to combat a combination of substitutions and synchronization errors in the ℓ-symbol read channel. Secondly, the RLP code can be viewed as a combination of an error correcting code and a constrained code avoiding a set of patterns and as such they pose several interesting theoretical challenges in combinatorics, algorithms and coding theory. Finally, from the practical point of view, these sequences and codes have numerous applications, especially error-correction in racetrack memories.Owing to their applications in racetrack memories, we investigate (n, d, b, k) <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</inf> RLP codes with small values of b and d. Numerous techniques are used to compute the maximal asymptotic rate (capacity) of these codes for given set of parameters. The numerical results are computed and tabulated. We also study these codes with large values of b and d for theoretical interests. In some cases, we can construct a code with only a single bit of redundancy.
Universal Compression of Large Alphabets with Constrained Compressors

Over unknown, possibly large, alphabets, one approach for compressing sequences is to separately convey their symbols and patterns (sequences of integers representing orders in which the symbols appear). It has been shown that patterns generated by i.i.d. sources can be compressed with diminishing redundancy using compressors that know the number of occurrences of each integer symbol. Motivated by applications with resource restrictions, e.g., data deduplication, we study universal compression of patterns using compressors under constraints. A characterization of constrained compressors is given and general results for computing redundancies are derived. We also show that for patterns generated by i.i.d. sources over an alphabet of size k, the per-symbol average- and worst-case redundancies are at least Θ(log(min(k, n/ logn))) bits (n is the sequence length), under the constraint that compressors only know the number of distinct integer symbols in the pattern. A simple sequential compressor satisfying this constraint is also analyzed and shown to achieve this redundancy in the first order term.
Analytic Mutual Information in Bayesian Neural Networks

Bayesian neural networks have successfully designed and optimized a robust neural network model in many application problems, including uncertainty quantification. However, with its recent success, information-theoretic understanding about the Bayesian neural network is still at an early stage. Mutual information is an example of an uncertainty measure in a Bayesian neural network to quantify epistemic uncertainty. Still, no analytic formula is known to describe it, one of the fundamental information measures to understand the Bayesian deep learning framework. In this paper, we derive the analytical formula of the mutual information between model parameters and the predictive output by leveraging the notion of the point process entropy. Then, as an application, we discuss the parameter estimation of the Dirichlet distribution and show its practical application in the active learning uncertainty measures by demonstrating that our analytical formula can improve the performance of active learning further in practice.
On Fisher Information Matrix for Simple Neural Networks with Softplus Activation

Fisher information of simple neural networks with the softplus activation function is argued. We show that, under certain conditions, FIMs of simple models have the similar interesting spectral structure as the one shown by Takeishi et al (2021) for networks with ReLU. This work helps us to understand why the FIM has such the structure.
Estimating Sparse Distributions Under Joint Communication and Privacy Constraints

We consider the problem of estimating a d-dimensional, s-sparse discrete distribution from independent samples subject to a joint b-bit communication constraint and ε-local differential privacy constraint. As an intermediate step, we introduce the Privatized Random Hashing (PRH) scheme, which concatenates a hashing-based quantization strategy with the randomized response privacy mechanism. Despite its simplicity, PRH turns out to achieve the order-optimal minimax estimation error and sample complexity in the standard (non-sparse) estimation setting, for all communication and privacy regimes. We then address the sparse case by developing a two-stage, non-interactive estimation scheme based on PRH in which the first half of samples are used to localize the unknown support of the distribution, and the remaining samples are used to obtain precise estimates of the individual probabilities. Using this scheme, we characterize the minimax sample complexity of the sparse case up to logarithmic factors, unifying existing results in the literature that considered communication and privacy constraints separately.
Constrained Obfuscation to Thwart Pattern Matching Attacks

Recently, we have proposed a model-free privacy-preserving mechanism (PPM) against attacks that compromise user privacy by matching patterns in data sequences to those that are unique to a given user [1]. Because the PPM is model-free, there are no requirements on the statistical model for the data, which is desirable when the model is not perfectly known. However, the proposed PPM did not enforce any constraints on the value to which a data point might be obfuscated, hence allowing an unlikely pattern that would make it easy for the adversary to detect which values have been obfuscated. In this paper, we consider a constrained PPM that enforces a continuity constraint so as to avoid abrupt jumps in the obfuscated data. To design such, we employ a graph-based analytical framework and the concept of consecutive patterns. At each point, the obfuscated data should be chosen strictly from that point’s neighbors. Unfortunately, this might undesirably increase the noise level employed in data obfuscation and hence unacceptably reduce utility. We propose a new obfuscation algorithm, namely the obfuscation-return algorithm, and characterize its privacy guarantees under continuity and noise level constraints.
Deterministic Encoding into Generalized Huff curves

We propose a novel encoding function for hashing into generalized Huff curves. This encoding is based on computing the square root, and is almost injective. An elimination trick is introduced to cut down inversions in encoding function. On F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P256</inf> , our encoding is over three times faster than the state-of-the-art encoding in constant time implementation.
Fundamental Limits of Personalized Federated Linear Regression with Data Heterogeneity

Federated learning is a nascent framework for collaborative machine learning over networks of devices with local data and local model updates. Data heterogeneity across the devices is one of the challenges confronting this emerging field. Personalization is a natural approach to simultaneously utilize information from the other users’ data and take data heterogeneity into account. In this work, we study the linear regression problem where the data across users are generated from different regression vectors. We present an information-theoretic lower bound of the minimax expected excess risk of personalized linear models. We show an upper bound that matches the lower bound within constant factors. The results characterize the effect of data heterogeneity on learning performance and the trade-off between sample size, problem difficulty, and distribution discrepancy, suggesting that the discrepancy-to-difficulty ratio is the key factor governing the effectiveness of heterogeneous data.
Finite Littlestone Dimension Implies Finite Information Complexity

We prove that every online learnable class of functions of Littlestone dimension d admits a learning algorithm with finite information complexity. Towards this end, we use the notion of a globally stable algorithm. Generally, the information complexity of such a globally stable algorithm is large yet finite, roughly exponential in d. We also show there is room for improvement; for a canonical online learnable class, indicator functions of affine subspaces of dimension d, the information complexity can be upper bounded logarithmically in d.
Co-design of CSS Codes and Diagonal Gates

The challenge of quantum computing is to combine error resilience with universal computation. There are many finite sets of gates that are universal, and a standard choice is to augment the set of Clifford gates by a non-Clifford unitary such as the T gate. Given a CSS code, we introduce a method of synthesizing all possible diagonal physical gates that preserve the codespace and induce a target logical gate. We denote an 〚n, k = k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> − k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , d〛 CSS code $\mathcal{C}$ by $\operatorname{CSS} \left( {X,{\mathcal{C}_2};Z,\mathcal{C}_1^ \bot } \right)$, where the [n, k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ] binary code ${\mathcal{C}_2}$ determines the X-stabilizers in $\mathcal{C}$, and the [n, n−k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ] binary code $\mathcal{C}_1^ \bot $ determines the Z-stabilizers in $\mathcal{C}$. The diagonal entries of a diagonal physical gate are indexed by binary vectors in $\mathbb{F}_2^n$. We show that a diagonal physical gate preserves the CSS codespace if and only if entries from the same coset of ${\mathcal{C}_2}$ in ${\mathcal{C}_1}$ (same X-logical) are identical. We also show that the target logical operator only specifies ${2^{{k_1}}}$ out of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> diagonal entries of the diagonal physical gate. The remaining degrees of freedom can be used to optimize implementation of the physical gate within a particular quantum computing infrastructure. This encompasses optimization with respect to locality of the physical gate, a criterion that is essential to fault tolerance. When the target logical operator is the identity, the physical gates that preserve the CSS code represent noise operators to which the codespace is oblivious. We illustrate our method by providing several examples of code-gate pairs for which the target logical gate is a non-Clifford unitary. The framework is extended to stabilizer codes in https://arxiv.org/abs/2109.13481.
On the Security Properties of Combinatorial All-or-nothing Transforms

All-or-nothing transforms (AONT) were proposed by Rivest as a message preprocessing technique for encrypting data to protect against brute-force attacks, and have many applications in cryptography and information security. Later the unconditionally secure AONT and their combinatorial characterization were introduced by Stinson. Informally, a combinatorial AONT is an array with the unbiased requirements and its security properties in general depend on the prior probability distribution on the inputs s-tuples. Recently, it was shown by Esfahani and Stinson that a combinatorial AONT has perfect security provided that all the inputs s-tuples are equiprobable, and has weak security provided that all the inputs s-tuples are with non-zero probability. This paper aims to explore on the gap between perfect security and weak security for combinatorial (t, s, v)-AONTs. Concretely, we consider the typical scenario that all the s inputs take values independently (but not necessarily identically) and quantify the amount of information $H(\mathcal{X}\mid \mathcal{Y})$ about any t inputs $\mathcal{X}$ that is not revealed by any s−t outputs $\mathcal{Y}$. In particular, we establish the general lower and upper bounds on $H(\mathcal{X}\mid \mathcal{Y})$ for combinatorial AONTs using information-theoretic techniques, and also show that the derived bounds can be attained in certain cases.
Blogging as Popular Scholarship

Abstract Blogging has become an increasingly common and popular way of disseminating information. However, academia has been slow to embrace blogging as a publication option, likely because blogging does not rely on the traditional process of peer review that other forms of academic scholarship require. This chapter discusses the rise of blogging as popular scholarship, highlighting blogs that have been successful at gaining a wide audience. It interrogates what constitutes scholarship, considering arguments both for and against considering blogs as scholarly writing. The chapter also discusses the potential advantages and disadvantages of blogging, suggests processes for evaluating blogs as scholarship in the hiring and reappointment, promotion, and tenure (RPT) process, and proposes how academia in general and the Society for Music Theory (SMT) in particular can provide support for blogging as an effective means of outreach and of making music theory research accessible to other scholars and the general public.
A privacy data security scheme based on homomorphic encryption and consortium blockchain for the Internet of Things

The user's private data is very important in data management of the Internet of Things (IoT). In the traditional centralized architecture of the IoT, problems such as data leakage, illegal tampering and information island have gradually become the shackles that restrict the wide expansion of the IoT.This paper proposes a new secure scheme of the private data based on consortium blockchain for the IoT, which allows users to achieve secure storage and data sharing of the private data. At the same time, after the IPFS system generates identification of the data and the secondary encryption by smart contract of the homomorphic encryption algorithm, it will greatly enhance the storage capacity and security for the private data. Security analysis shows that the scheme will further improve robustness of the network, reduce consumption of energy and protect the rights of users. Performance analysis shows that the storage capability of IPFS and the processing efficiency of the homomorphic encryption algorithm are in the millisecond level, which can satisfy the practical requirements of the IoT.
Time Words and Their Regular Representation

Time words are a kind of complex words with their own unique rules of word formation. The number of time words in the open source word vector corpus is large, the structure is complex, and there is a significant long-tail phenomenon, that is, with the increase of word segmentation materials, the number of time words also increases simultaneously, and more low-frequency words appear, which gives the word vector Representation and application bring difficulties. In order to obtain a better representation of time words, this paper investigates the compositional characteristics of time words in open source word vector data, and proposes a rule-based method to classify time words. The rules can be used to analyze the cause of the difficulty in representing time words. In the above, a new time word representation method is proposed, which has the potential to improve time word representation and application difficulties.