Reviewerly/exHarmony huggingface dataset:
Survey on A User Authentication Scheme Using Block Chain-Enabled Fog Nodes

The main objective of this project is securely store and maintain the data using block chain.The blockchain technology is used to protect the healthcare data hosted within the cloud.The block that contain the data and the time stamp.It allows healthcare provider to access the IOT data more securely from anywhere.we propose a user authentication scheme using block chain-enabled fog nodes in which fog nodes interface to Ethereum smart contracts to authenticate users to access IoT devices.Block chain is fundamentally a decentralized, distributed, shared, and immutable database ledger that stores registry of assets and transactions across peer-to-peer (P2P) network It preserve data from attackers.The data is encrypted prior to outsourcing to the cloud.The healthcare provider have to decrypt the data prior to download.
Implementation on A User Authentication Scheme Using Block Chain-Enabled Fog Nodes

The primary goal of this project is to securely store and maintain healthcare data using blockchain technology.By leveraging the security features of blockchain, the healthcare data hosted within the cloud can be protected.Each block contains the data and a timestamp, which allows healthcare providers to securely access IoT data from anywhere.We propose a user authentication scheme that uses blockchain-enabled fog nodes.These fog nodes interface with Ethereum smart contracts to authenticate users who want to access IoT devices.Blockchain is a decentralized, distributed, shared, and immutable database ledger that stores a registry of assets and transactions across a peer-to-peer (P2P) network.It ensures that the data is preserved from attackers.Before outsourcing the data to the cloud, it is encrypted to ensure its privacy.The healthcare provider must decrypt the data before downloading it.
Design of Earthquake Warning Alarm Using Accelerometer Sensor Based on Internet of Things

Earthquake is natural event due to release of energy suddenly from within that creates seismic waves. The vibrations are usually caused by movement of earth's plates. It cannot be predicted when they are coming. Therefore, need to build an early warning system. It is for anticipation of save the community when an earthquake occurs. It provides solutions to minimize the impact of earthquake events. This system uses accelerometer sensor and arduino nano as a main controller and it is accompanied by Blynk application. This works for sent notification via smartphone. This tool has been successfully designed with manual testing to be able to see the sensitivity of earthquake sensor made. Accelerometer sensor works is using Tilt Method. Accelerometer sensor will tilt on surface as parable of earthquake. Accelerometer sensor will send vibration signal when earthquake occurs. Signal processing is done by Arduino nano as the main controller in system. If the signal is received by sensor then value of earthquake will be displayed on LCD screen. Status of earthquake was separated into 4 levels such as standby, alert, dangerous, dangerous and dangerous. After earthquake value appears on the LCD screen and the status is known then buzzer will sound. It indicates that the earthquake warning alarm is properly functioning. Next, NodeMCU also sends information to Blynk application, which is used as a notification and monitoring of earthquake conditions. Buzzer will sound as a warning alarm to notify peoples to save themselves. This sensor has three output coordinate points, namely X, Y, Z, which have analog values ​​on Arduino Nano. This analog value can be converted into Richter Scale. Based on experimental results, an analog value is 100 – 400.
Strategies for Developing Holistic University Students within the Framework of Five-dimensional Education Integration in China: Status, Concept, and Realization Paths

Research on the integration of Five-Dimensional Education is currently underway, encompassing both theoretical and practical aspects.The production of fruitful outcomes represents a significant challenge in the transformation and advancement of primary education in contemporary China.There exists a dearth of scholarly inquiry in the realm of higher education.This study expands upon the integration of Five-Dimensional Education into higher education and investigates the notion of holistic university students, as well as the methods for fostering such individuals.The study's results demonstrate the present state and predicaments regarding the execution of the Five-dimensional Education Integration in Chinese higher education institutions within the contemporary context.Additionally, the study expounds upon the concept and definition of holistic university students and puts forth five strategies for fostering holistic university students within the Five Education Integration framework.The proposed strategies aim to incorporate five-dimensional education into curriculum design, utilise diverse teaching methods to facilitate the integration of five education, implement experiential learning to augment social responsibility and innovation, establish a discipline system that prioritises student needs, and establish an effective student management mechanism.The conclusion proposes that further investigation is warranted into the notion of holistic university students within the Chinese context, with the aim of enhancing comprehension of this educational concept among both educators and learners.The subsequent section of the article proposes that additional investigation ought to be conducted regarding the attributes that holistic university students ought to possess, and more practical methods of fostering such attributes should be put forth.
THE IMPORTANCE OF MUSICAL CIRCLES IN EDUCATION OF HIGH SCHOOLSTUDENTS

Speaking about amateur art circles, it should be noted that these are mainly musical circles that solve various problems: they develop artistic creativity, musical abilities, form aesthetic tastes, teach you to express your feelings through music, dance and facial expressions, help to identify the most gifted in the field of art children, help children rationally use their free time. In the article, the author determined the content, specifics and effective forms of work of musical circles, investigated the requirements for the work of musical circles, the ways and possibilities of connecting circle work with the process of teaching music lessons in high school, developed a number of recommendations for the pedagogical management of musical circles of school amateur performances and planning group work systems.
Application of Blockchain Technology at Sekolah Islam Terpadu Al-Madinah

Abstract: The purpose of this study is to discuss Blockchain Technology and the application of Sisko at the Al-Madinah Integrated Islamic School, where the students’ cognitive development varies from 4 years to 17 years old. This article uses the library research method which refers to 19 articles downloaded from Google Scholar and Crossref. Blockchain technology is a system that limits its users according to its boundaries but can still be connected with other users. One of the systems for implementing Blockchain Technology at the Sekolah Islam Terpadu Al-Madinah is Sisko SIT Al-Madinah, which contains a database about education from Kindergarten to High School within the Al-Madinah Integrated Islamic School. The menu in the Sisko system is different for each user, for example students use Sisko for exams and assignments, teachers use Sisko to input grades and savings, principals and vice principals use Sisko to manage curriculum, add and edit student data, map students into classes, and finally the Sisko admin uses it to add and edit report formats, map user accounts and archive school databases. Sisko is highly recommended for schools that want to be more effective and efficient in managing data, finances, learning outcomes and in archiving all of their data because schools must be ready to shift into school digitalization in this era.
The Role Of Psychology In Overcoming Student Borneo In Learning

This study aims to see the role of psychology in overcoming student saturation in learning. Learning is an activity carried out by an individual every day, but in learning, various problems are often found in the process of changing behavior. One of them is the feeling of boredom that is experienced when carrying out the learning process. The method used in this research is qualitative research with library research. The results showed that the problem of learning saturation experienced by students was one of the obstacles that made the quality of student learning outcomes low. Factors causing boredom in learning include the lack of motivation in the learning process, the low creativity of the teacher in presenting interesting material in the learning process. So that this does not become a disturbance in the student learning process, the role of psychology is used in overcoming the saturation experienced by students in learning.
Dynamic programming by polymorphic semiring algebraic shortcut fusion

Dynamic programming (DP) is an algorithmic design paradigm for the efficient, exact solution of otherwise intractable, combinatorial problems. However, DP algorithm design is often presented in an ad-hoc manner. It is sometimes difficult to justify algorithm correctness. To address this issue, this paper presents a rigorous algebraic formalism for systematically deriving DP algorithms, based on semiring polymorphism. We start with a specification, construct an algorithm to compute the required solution which is self-evidently correct because it exhaustively generates and evaluates all possible solutions meeting the specification. We then derive, through the use of shortcut fusion, an implementation of this algorithm which is both efficient and correct. We also demonstrate how, with the use of semiring lifting, the specification can be augmented with combinatorial constraints, showing how these constraints can be fused with the algorithm. We furthermore demonstrate how existing DP algorithms for a given combinatorial problem can be abstracted from their original context and re-purposed. This approach can be applied to the full scope of combinatorial problems expressible in terms of semirings. This includes, for example: optimal probability and Viterbi decoding, probabilistic marginalization, logical inference, fuzzy sets, differentiable softmax, relational and provenance queries. The approach, building on ideas from the existing literature on constructive algorithmics, exploits generic properties of polymorphic functions, tupling and formal sums and algebraic simplifications arising from constraint algebras. We demonstrate the effectiveness of this formalism for some example applications arising in signal processing, bioinformatics and reliability engineering. Python software implementing these algorithms can be downloaded from: http://www.maxlittle.net/software/dppolyalg.zip.
Some aspects of noise in binary classification with quantum circuits

We formally study the effects of a restricted single-qubit noise model inspired by real quantum hardware, and corruption in quantum training data, on the performance of binary classification using quantum circuits. We find that, under the assumptions made in our noise model, that the measurement of a qubit is affected only by the noises on that qubit even in the presence of entanglement. Furthermore, when fitting a binary classifier using a quantum dataset for training, we show that noise in the data can work as a regularizer, implying potential benefits from the noise in certain cases for machine learning problems.
Provable Preimage Under-Approximation for Neural Networks

Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set. However, often it is important to know whether a given property holds globally for the input domain, and if not then for what proportion of the input the property is true. To analyze such properties requires computing preimage abstractions of neural networks. In this work, we propose an efficient anytime algorithm for generating symbolic under-approximations of the preimage of any polyhedron output set for neural networks. Our algorithm combines a novel technique for cheaply computing polytope preimage under-approximations using linear relaxation, with a carefully-designed refinement procedure that iteratively partitions the input region into subregions using input and ReLU splitting in order to improve the approximation. Empirically, we validate the efficacy of our method across a range of domains, including a high-dimensional MNIST classification task beyond the reach of existing preimage computation methods. Finally, as use cases, we showcase the application to quantitative verification and robustness analysis. We present a sound and complete algorithm for the former, which exploits our disjoint union of polytopes representation to provide formal guarantees. For the latter, we find that our method can provide useful quantitative information even when standard verifiers cannot verify a robustness property.
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula

Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme. The robotics community has experimented autocurricular training with physically grounded problems, such as robust control and interactive manipulation tasks. However, the asymmetric nature of these tasks makes the generation of sophisticated policies challenging. Indeed, the asymmetry in the environment may implicitly or explicitly provide an advantage to a subset of agents which could, in turn, lead to a low-quality equilibrium. This paper proposes a novel game-theoretic algorithm, Stackelberg Multi-Agent Deep Deterministic Policy Gradient (ST-MADDPG), which formulates a two-player MARL problem as a Stackelberg game with one player as the `leader' and the other as the `follower' in a hierarchical interaction structure wherein the leader has an advantage. We first demonstrate that the leader's advantage from ST-MADDPG can be used to alleviate the inherent asymmetry in the environment. By exploiting the leader's advantage, ST-MADDPG improves the quality of a co-evolution process and results in more sophisticated and complex strategies that work well even against an unseen strong opponent.
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming

Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning. In contrast to works that rely on hand-crafted logic rules, our differentiable symbolic reasoning framework efficiently learns weighted rules and applies semantic loss to further improve LMs. DSR-LM is scalable, interpretable, and allows easy integration of prior knowledge, thereby supporting extensive symbolic programming to robustly derive a logical conclusion. The results of our experiments suggest that DSR-LM improves the logical reasoning abilities of pre-trained language models, resulting in a significant increase in accuracy of over 20% on deductive reasoning benchmarks. Furthermore, DSR-LM outperforms a variety of competitive baselines when faced with systematic changes in sequence length.
Differentially-private Continual Releases against Dynamic Databases

Prior research primarily examined differentially-private continual releases against data streams, where entries were immutable after insertion. However, most data is dynamic and housed in databases. Addressing this literature gap, this article presents a methodology for achieving differential privacy for continual releases in dynamic databases, where entries can be inserted, modified, and deleted. A dynamic database is represented as a changelog, allowing the application of differential privacy techniques for data streams to dynamic databases. To ensure differential privacy in continual releases, this article demonstrates the necessity of constraints on mutations in dynamic databases and proposes two common constraints. Additionally, it explores the differential privacy of two fundamental types of continual releases: Disjoint Continual Releases (DCR) and Sliding-window Continual Releases (SWCR). The article also highlights how DCR and SWCR can benefit from a hierarchical algorithm for better privacy budget utilization. Furthermore, it reveals that the changelog representation can be extended to dynamic entries, achieving local differential privacy for continual releases. Lastly, the article introduces a novel approach to implement continual release of randomized responses.
Persistent Homology Meets Object Unity: Object Recognition in Clutter

Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. To address this challenge, we propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. The descriptor employs a novel slicing-based approach to compute topological features from filtrations of simplicial complexes using persistent homology, and facilitates reasoning-based recognition using object unity. Apart from a benchmark dataset, we report performance on a new dataset, the UW Indoor Scenes (UW-IS) Occluded dataset, curated using commodity hardware to reflect real-world scenarios with different environmental conditions and degrees of object occlusion. THOR outperforms state-of-the-art methods on both the datasets and achieves substantially higher recognition accuracy for all the scenarios of the UW-IS Occluded dataset. Therefore, THOR, is a promising step toward robust recognition in low-cost robots, meant for everyday use in indoor settings.
CLaC at SemEval-2023 Task 2: Comparing Span-Prediction and Sequence-Labeling approaches for NER

This paper summarizes the CLaC submission for the MultiCoNER 2 task which concerns the recognition of complex, fine-grained named entities. We compare two popular approaches for NER, namely Sequence Labeling and Span Prediction. We find that our best Span Prediction system performs slightly better than our best Sequence Labeling system on test data. Moreover, we find that using the larger version of XLM RoBERTa significantly improves performance. Post-competition experiments show that Span Prediction and Sequence Labeling approaches improve when they use special input tokens (
 and 
) of XLM-RoBERTa. The code for training all models, preprocessing, and post-processing is available at https://github.com/harshshredding/semeval2023-multiconer-paper.
An Investigation into the Impacts of Deep Learning-based Re-sampling on Specific Emitter Identification Performance

Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that a majority of IoT devices use weak or no encryption at all. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while simultaneously reducing the hardware requirements of the IoT devices that collect them. DL-driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network only approach.
Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages

In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation engine. For example, healthcare records, hygienic procedures, government communication, emergency procedures and religious texts are all limited texts. While generic translation engines for all languages do not exist, translation of multilingually known limited texts into new, endangered languages may be possible and reduce human translation effort. We attempt to leverage translation resources from many rich resource languages to efficiently produce best possible translation quality for a well known text, which is available in multiple languages, in a new, severely low resource language. We examine two approaches: 1. best selection of seed sentences to jump start translations in a new language in view of best generalization to the remainder of a larger targeted text(s), and 2. we adapt large general multilingual translation engines from many other languages to focus on a specific text in a new, unknown language. We find that adapting large pretrained multilingual models to the domain/text first and then to the severely low resource language works best. If we also select a best set of seed sentences, we can improve average chrF performance on new test languages from a baseline of 21.9 to 50.7, while reducing the number of seed sentences to only around 1,000 in the new, unknown language.
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning

Standard approaches to sequential decision-making exploit an agent's ability to continually interact with its environment and improve its control policy. However, due to safety, ethical, and practicality constraints, this type of trial-and-error experimentation is often infeasible in many real-world domains such as healthcare and robotics. Instead, control policies in these domains are typically trained offline from previously logged data or in a growing-batch manner. In this setting a fixed policy is deployed to the environment and used to gather an entire batch of new data before being aggregated with past batches and used to update the policy. This improvement cycle can then be repeated multiple times. While a limited number of such cycles is feasible in real-world domains, the quality and diversity of the resulting data are much lower than in the standard continually-interacting approach. However, data collection in these domains is often performed in conjunction with human experts, who are able to label or annotate the collected data. In this paper, we first explore the trade-offs present in this growing-batch setting, and then investigate how information provided by a teacher (i.e., demonstrations, expert actions, and gradient information) can be leveraged at training time to mitigate the sample complexity and coverage requirements for actor-critic methods. We validate our contributions on tasks from the DeepMind Control Suite.
Unifying Pointer Analyses for Polyglot Inter-operations through Summary Specialization

Modular analysis of polyglot applications is challenging because heap object flows across language boundaries must be resolved. The state-of-the-art analyses for polyglot applications have two fundamental limitations. First, they assume explicit boundaries between the host and the guest language to determine inter-language dataflows. Second, they rely on specific analyses of the host and guest languages. The former assumption is impractical concerning recent advancements in polyglot programming techniques, while the latter disregards advances in pointer analysis of the underlying languages. In this work, we propose to extend existing pointer analyses with a novel summary specialization technique so that points-to set across language boundaries can be unified. Our novel technique leverages various combinations of host and guest analyses with minor modifications. We demonstrate the efficacy and generalizability of our approach by evaluating it with two polyglot language models: Java-C communication via Android's NDK and Java-Python communication in GraalVM.