Reviewerly/exHarmony huggingface dataset:
LERT: A Linguistically-motivated Pre-trained Language Model

Pre-trained Language Model (PLM) has become a representative foundation model in the natural language processing field. Most PLMs are trained with linguistic-agnostic pre-training tasks on the surface form of the text, such as the masked language model (MLM). To further empower the PLMs with richer linguistic features, in this paper, we aim to propose a simple but effective way to learn linguistic features for pre-trained language models. We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy. We carried out extensive experiments on ten Chinese NLU tasks, and the experimental results show that LERT could bring significant improvements over various comparable baselines. Furthermore, we also conduct analytical experiments in various linguistic aspects, and the results prove that the design of LERT is valid and effective. Resources are available at https://github.com/ymcui/LERT
Contrastive Self-Supervised Learning for Skeleton Representations

Human skeleton point clouds are commonly used to automatically classify and predict the behaviour of others. In this paper, we use a contrastive self-supervised learning method, SimCLR, to learn representations that capture the semantics of skeleton point clouds. This work focuses on systematically evaluating the effects that different algorithmic decisions (including augmentations, dataset partitioning and backbone architecture) have on the learned skeleton representations. To pre-train the representations, we normalise six existing datasets to obtain more than 40 million skeleton frames. We evaluate the quality of the learned representations with three downstream tasks: skeleton reconstruction, motion prediction, and activity classification. Our results demonstrate the importance of 1) combining spatial and temporal augmentations, 2) including additional datasets for encoder training, and 3) and using a graph neural network as an encoder.
Modularized and Scalable Compilation for Double Quantum Dot Quatum Computing

Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make this very challenging. In this paper, based on the quantum variational algorithm, we propose a novel scheme to train an Ansatz circuit and realize high-fidelity compilation of a set of universal quantum gates for singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily constrained system. Furthermore, we propose a scalable architecture for a modular implementation of quantum programs in this constrained systems and validate its performance with two representative demonstrations, Grover's algorithm for the database searching (static compilation) and a variant of variational quantum eigensolver for the Max-Cut optimization (dynamic compilation). Our methods are potentially applicable to a wide range of physical devices. This work constitutes an important stepping-stone for exploiting the potential for advanced and complicated quantum algorithms on near-term devices.
A classification performance evaluation measure considering data separability

Machine learning and deep learning classification models are data-driven, and the model and the data jointly determine their classification performance. It is biased to evaluate the model's performance only based on the classifier accuracy while ignoring the data separability. Sometimes, the model exhibits excellent accuracy, which might be attributed to its testing on highly separable data. Most of the current studies on data separability measures are defined based on the distance between sample points, but this has been demonstrated to fail in several circumstances. In this paper, we propose a new separability measure--the rate of separability (RS), which is based on the data coding rate. We validate its effectiveness as a supplement to the separability measure by comparing it to four other distance-based measures on synthetic datasets. Then, we demonstrate the positive correlation between the proposed measure and recognition accuracy in a multi-task scenario constructed from a real dataset. Finally, we discuss the methods for evaluating the classification performance of machine learning and deep learning models considering data separability.
MSDT: Masked Language Model Scoring Defense in Text Domain

Pre-trained language models allowed us to process downstream tasks with the help of fine-tuning, which aids the model to achieve fairly high accuracy in various Natural Language Processing (NLP) tasks. Such easily-downloaded language models from various websites empowered the public users as well as some major institutions to give a momentum to their real-life application. However, it was recently proven that models become extremely vulnerable when they are backdoor attacked with trigger-inserted poisoned datasets by malicious users. The attackers then redistribute the victim models to the public to attract other users to use them, where the models tend to misclassify when certain triggers are detected within the training sample. In this paper, we will introduce a novel improved textual backdoor defense method, named MSDT, that outperforms the current existing defensive algorithms in specific datasets. The experimental results illustrate that our method can be effective and constructive in terms of defending against backdoor attack in text domain. Code is available at https://github.com/jcroh0508/MSDT.
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts

Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autonomous vehicles. The main challenge associated with self-learned agents in the form of deep neural networks, is their black-box nature: it is impossible for humans to interpret deep neural networks. Therefore, humans cannot directly interpret the actions of deep neural network based agents, or foresee their robustness in different scenarios. In this work, we demonstrate a method for probing which concepts self-learning agents internalise in the course of their training. For demonstration, we use a chess playing agent in a fast and light environment developed specifically to be suitable for research groups without access to enormous computational resources or machine learning models.
Black-Hole Radiation Decoding is Quantum Cryptography

We propose to study equivalence relations between phenomena in high-energy physics and the existence of standard cryptographic primitives, and show the first example where such an equivalence holds. A small number of prior works showed that high-energy phenomena can be explained by cryptographic hardness. Examples include using the existence of one-way functions to explain the hardness of decoding black-hole Hawking radiation (Harlow and Hayden 2013, Aaronson 2016), and using pseudorandom quantum states to explain the hardness of computing AdS/CFT dictionary (Bouland, Fefferman and Vazirani, 2020). In this work we show, for the former example of black-hole radiation decoding, that it also implies the existence of secure quantum cryptography. In fact, we show an existential equivalence between the hardness of black-hole radiation decoding and a variety of cryptographic primitives, including bit-commitment schemes and oblivious transfer protocols (using quantum communication). This can be viewed (with proper disclaimers, as we discuss) as providing a physical justification for the existence of secure cryptography. We conjecture that such connections may be found in other high-energy physics phenomena.
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions

Current state-of-the-art deep neural networks for image classification are made up of 10 - 100 million learnable weights and are therefore inherently prone to overfitting. The complexity of the weight count can be seen as a function of the number of channels, the spatial extent of the input and the number of layers of the network. Due to the use of convolutional layers the scaling of weight complexity is usually linear with regards to the resolution dimensions, but remains quadratic with respect to the number of channels. Active research in recent years in terms of using multigrid inspired ideas in deep neural networks have shown that on one hand a significant number of weights can be saved by appropriate weight sharing and on the other that a hierarchical structure in the channel dimension can improve the weight complexity to linear. In this work, we combine these multigrid ideas to introduce a joint framework of multigrid inspired architectures, that exploit multigrid structures in all relevant dimensions to achieve linear weight complexity scaling and drastically reduced weight counts. Our experiments show that this structured reduction in weight count is able to reduce overfitting and thus shows improved performance over state-of-the-art ResNet architectures on typical image classification benchmarks at lower network complexity.
Perfectly predicting ICU length of stay: too good to be true

A paper of Alsinglawi et al was recently accepted and published in Scientific Reports. In this paper, the authors aim to predict length of stay (LOS), discretized into either long (> 7 days) or short stays (< 7 days), of lung cancer patients in an ICU department using various machine learning techniques. The authors claim to achieve perfect results with an Area Under the Receiver Operating Characteristic curve (AUROC) of 100% with a Random Forest (RF) classifier with ADASYN class balancing over sampling technique, which if accurate could have significant implications for hospital management. However, we have identified several methodological flaws within the manuscript which cause the results to be overly optimistic and would have serious consequences if used in a clinical practice. Moreover, the reporting of the methodology is unclear and many important details are missing from the manuscript, which makes reproduction extremely difficult. We highlight the effect these oversights have had on the result and provide a more believable result of 88.91% AUROC when these oversights are corrected.
On Proper Learnability between Average- and Worst-case Robustness

Recently, Montasser et al. [2019] showed that finite VC dimension is not sufficient for proper adversarially robust PAC learning. In light of this hardness, there is a growing effort to study what type of relaxations to the adversarially robust PAC learning setup can enable proper learnability. In this work, we initiate the study of proper learning under relaxations of the worst-case robust loss. We give a family of robust loss relaxations under which VC classes are properly PAC learnable with sample complexity close to what one would require in the standard PAC learning setup. On the other hand, we show that for an existing and natural relaxation of the worst-case robust loss, finite VC dimension is not sufficient for proper learning. Lastly, we give new generalization guarantees for the adversarially robust empirical risk minimizer.
Complexity of Chess Domination Problems

We study different domination problems of attacking and non-attacking rooks and queens on polyominoes and polycubes of all dimensions. Our main result proves that maximal domination is NP-complete for non-attacking queens and for non-attacking rooks on polycubes of dimension three and higher. We also analyse these problems for polyominoes and convex polyominoes, conjecture the complexity classes and provide a computer tool for investigation. We have also computed new values for classical queen domination problems on chessboards (square polyominoes). For our computations, we have translated the problem into an integer linear programming instance. Finally, using this computational implementation and the game engine Godot, we have developed a video game of minimal domination of queens and rooks on randomly generated polyominoes.
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations

Interpretability provides a means for humans to verify aspects of machine learning (ML) models and empower human+ML teaming in situations where the task cannot be fully automated. Different contexts require explanations with different properties. For example, the kind of explanation required to determine if an early cardiac arrest warning system is ready to be integrated into a care setting is very different from the type of explanation required for a loan applicant to help determine the actions they might need to take to make their application successful. Unfortunately, there is a lack of standardization when it comes to properties of explanations: different papers may use the same term to mean different quantities, and different terms to mean the same quantity. This lack of a standardized terminology and categorization of the properties of ML explanations prevents us from both rigorously comparing interpretable machine learning methods and identifying what properties are needed in what contexts. In this work, we survey properties defined in interpretable machine learning papers, synthesize them based on what they actually measure, and describe the trade-offs between different formulations of these properties. In doing so, we enable more informed selection of task-appropriate formulations of explanation properties as well as standardization for future work in interpretable machine learning.
BERT in Plutarch's Shadows

The extensive surviving corpus of the ancient scholar Plutarch of Chaeronea (ca. 45-120 CE) also contains several texts which, according to current scholarly opinion, did not originate with him and are therefore attributed to an anonymous author Pseudo-Plutarch. These include, in particular, the work Placita Philosophorum (Quotations and Opinions of the Ancient Philosophers), which is extremely important for the history of ancient philosophy. Little is known about the identity of that anonymous author and its relation to other authors from the same period. This paper presents a BERT language model for Ancient Greek. The model discovers previously unknown statistical properties relevant to these literary, philosophical, and historical problems and can shed new light on this authorship question. In particular, the Placita Philosophorum, together with one of the other Pseudo-Plutarch texts, shows similarities with the texts written by authors from an Alexandrian context (2nd/3rd century CE).
Quantum Power Flows: From Theory to Practice

Climate change is becoming one of the greatest challenges to the sustainable development of modern society. Renewable energies with low density greatly complicate the online optimization and control processes, where modern advanced computational technologies, specifically quantum computing, have significant potential to help. In this paper, we discuss applications of quantum computing algorithms toward state-of-the-art smart grid problems. We suggest potential, exponential quantum speedup by the use of the Harrow-Hassidim-Lloyd (HHL) algorithms for sparse matrix inversions in power-flow problems. However, practical implementations of the algorithm are limited by the noise of quantum circuits, the hardness of realizations of quantum random access memories (QRAM), and the depth of the required quantum circuits. We benchmark the hardware and software requirements from the state-of-the-art power-flow algorithms, including QRAM requirements from hybrid phonon-transmon systems, and explicit gate counting used in HHL for explicit realizations. We also develop near-term algorithms of power flow by variational quantum circuits and implement real experiments for 6 qubits with a truncated version of power flows.
Realization of a multi-output quantum pulse gate for decoding high-dimensional temporal modes of single-photon states

Temporal modes (TMs) of photons provide an appealing high-dimensional encoding basis for quantum information. While techniques to generate TM states have been established, high-dimensional decoding of single-photon TMs remains an open challenge. In this work, we experimentally demonstrate demultiplexing of five-dimensional TMs of single photons with an average fidelity of 0.96 $\pm$ 0.01, characterized via measurement tomography. This is achieved using a newly developed device, the multi-output quantum pulse gate (mQPG). We demonstrate a proof-of-principle complete decoder based on the mQPG that operates on any basis from a set of 6 five-dimensional MUBs and is therefore suitable as a receiver for high-dimensional quantum key distribution. Furthermore, we confirm the high-quality operation of the mQPG by performing resource-efficient state tomography with an average fidelity of 0.98 $\pm$ 0.02.
Expeditious Cyber-Bullying Detection Method Utilizing Compact BERT Models

Since many people now use social media to disseminate hate, many researchers have been concentrating on the issue of detecting cyberbullying. in the last ten years. In this work, transfer learning is used to address this issue. We adjust our tiny BERT models using data from hate speech. We use the Focal Loss function to address the data's class imbalance. With this method, we were able to obtain cutting-edge outcomes on the hate speech dataset, including 0.91 precision, 0.92 recall, and 0.91 F1-score. Additionally, we demonstrate using our transfer learning pipeline that the more compact BERT models are much faster at detecting cyberbullying and are appropriate for real-time applications.
Differential Distribution Table implementation DDT survey

The security of the transmitted data is one of the important issues that led to improve cryptosystems. Block ciphers as symmetric key cryptosystems are widely implemented in different applications and devices. Many algorithms had been presented that proposed new designs and enhancements for the block ciphers. Most of these papers based on the differential characteristics that provided by the Differential Distribution table DDT. The implementation of DDT had several points of advantages in design, modify and attack block cipher. Even with larger block size of some cipher families, the Partial Differential Distribution Table (PDDT) is implemented to override the big block size. The comparison results showed that both of DDT and PDDT are efficient tools that provide important differential characteristics that may implemented with accompanied of different function and deep learning models.
A Lightweight Portable Multithreaded Client-Server Docker Containers

In terms of diversity in operating systems, environments, and platforms, and with limited host infrastructure resources to hold all operating systems and platforms, the need arises to design applications that run in many or we can say in all operating systems and platforms. This paper deals with designing and implementing a lightweight multithreaded client-server application appears as a separate Docker container. Both client and server containers are based on alpine Linux and developed using Python programming language. The execution unit in the containers is Python program files. As a case study, the server acts as a Wikipedia server and it can serve many clients simultaneously. The Docker will build the containers depending on a writing Dockerfile for each container and push them to the registry (docker hub). When pulled the image of the containers from the registry account, then it could be run the container on a host. The proposed containerized multithreaded client-server model will become a portable with limited capabilities other than using Virtualization. Resources and cost requirements to achieve portability is evaluated for both virtualization and containerization paradigms. The results showed the superiority of containerization over the virtualization in both resources and cost requirements.
THEORETICAL PRINCIPLES OF FORMING LEARNING MOTIVATION IN STUDENTS

The article reveals the specifics of achieving educational achievements based on the formation of positive motivation of students for educational activities. Theoretical and practical views on the motives of learning in the educational process are expressed.
METHODOLOGICAL FOUNDATIONS OF TEACHING PRIMARY SCHOOL STUDENTS TO ASK CORRECT QUESTION

The article outlines the methodological foundations of teaching elementary school students to ask correct questions, including the possibilities of critical thinking technology in developing the "art" of asking students questions.
Pengaruh Model Pembelajaran Role Playing Bermuatan Cerita Ramayana terhadap Hasil Belajar IPS

This study aims to analyze the differences in social studies learning outcomes between students who take lessons with the Role-Playing learning model containing the Ramayana story and students who follow conventional learning models for fifth grade students in elementary school Cluster VII, Buleleng District. This type of research is a quasi-experimental study with a population of all fifth-grade students of elementary school cluster VII Buleleng District. While the research sample amounted to 93 people. It can be concluded that the application of the Role-Playing learning model containing the Ramayana story has a positive effect on the social studies learning outcomes of fifth graders in the elementary school of Cluster VII, Buleleng District.
Sprites

Sprites are images or sets of images that are that in most cases – assigned to an object. Multiple images can be shown in order to create an animation. There are several functions for drawing and manipulating sprites.
Ict Competencies of a Teacher as an Element of Psycho-Logical Security of the Educational Environment

The ICT competence of a teacher is one of the key elements of the psychological security of the educational environment due to his authority in the eyes of students. Informatization of education carries both positive and negative effects, with which every modern teacher should be well acquainted. He must also have the ability to transform negative effects into positive ones or protect the student from them
dis assembly: Collaborative Rituals with an Autistic Artist

This article discusses a conversation with the author and Estée Klar, the founder of dis assembly, Klar’s autistic son, Adam Wolfond, mentors Chris Martin, Ellen Bleiwas, and Jessamyn Polson. dis assembly is a neurodiverse art collective for creation and relation in Toronto, Canada. Because public education in Canada typically does not provide supported/facilitated communication for non-speaking autists, this collective is ground-breaking in its experimental work with neurodivergent people. dis assembly highlights the need to honor the autistic ritualized use of objects and the environment as a valid and significant art form. The collective, therefore, re-imagines new forms of art, communication, interdependency, relationship, and community.