Reviewerly/exHarmony huggingface dataset:
FedMint: Intelligent Bilateral Client Selection in Federated Learning With Newcomer IoT Devices

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. We compare our approach against the VanillaFL selection process as well as other state-of-the-art approach and showcase the superiority of our proposal.
Avoiding symmetry roadblocks and minimizing the measurement overhead of adaptive variational quantum eigensolvers

Quantum simulation of strongly correlated systems is potentially the most feasible useful application of near-term quantum computers. Minimizing quantum computational resources is crucial to achieving this goal. A promising class of algorithms for this purpose consists of variational quantum eigensolvers (VQEs). Among these, problem-tailored versions such as ADAPT-VQE that build variational ansätze step by step from a predefined operator pool perform particularly well in terms of circuit depths and variational parameter counts. However, this improved performance comes at the expense of an additional measurement overhead compared to standard VQEs. Here, we show that this overhead can be reduced to an amount that grows only linearly with the number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> of qubits, instead of quartically as in the original ADAPT-VQE. We do this by proving that operator pools of size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn><mml:mi>n</mml:mi><mml:mo>&amp;#x2212;</mml:mo><mml:mn>2</mml:mn></mml:math> can represent any state in Hilbert space if chosen appropriately. We prove that this is the minimal size of such "complete" pools, discuss their algebraic properties, and present necessary and sufficient conditions for their completeness that allow us to find such pools efficiently. We further show that, if the simulated problem possesses symmetries, then complete pools can fail to yield convergent results, unless the pool is chosen to obey certain symmetry rules. We demonstrate the performance of such symmetry-adapted complete pools by using them in classical simulations of ADAPT-VQE for several strongly correlated molecules. Our findings are relevant for any VQE that uses an ansatz based on Pauli strings.
Optimised Trotter decompositions for classical and quantum computing

Abstract Suzuki–Trotter decompositions of exponential operators like <?CDATA $\exp(Ht)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>exp</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>H</mml:mi> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> are required in almost every branch of numerical physics. Often the exponent under consideration has to be split into more than two operators <?CDATA $H = \sum_k A_k$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>H</mml:mi> <mml:mo>=</mml:mo> <mml:mrow> <mml:munder> <mml:mo>∑</mml:mo> <mml:mi>k</mml:mi> </mml:munder> </mml:mrow> <mml:msub> <mml:mi>A</mml:mi> <mml:mi>k</mml:mi> </mml:msub> </mml:math> , for instance as local gates on quantum computers. We demonstrate how highly optimised schemes originally derived for exactly two operators <?CDATA $A_{1,2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>A</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> can be applied to such generic Suzuki–Trotter decompositions, providing a formal proof of correctness as well as numerical evidence of efficiency. A comprehensive review of existing symmetric decomposition schemes up to order <?CDATA $n\leqslant4$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>⩽</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> is presented and complemented by a number of novel schemes, including both real and complex coefficients. We derive the theoretically most efficient unitary and non-unitary 4th order decompositions. The list is augmented by several exceptionally efficient schemes of higher order <?CDATA $n\leqslant8$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>n</mml:mi> <mml:mo>⩽</mml:mo> <mml:mn>8</mml:mn> </mml:math> . Furthermore we show how Taylor expansions can be used on classical devices to reach machine precision at a computational effort at which state of the art Trotterization schemes do not surpass a relative precision of 10 −4 . Finally, a short and easily understandable summary explains how to choose the optimal decomposition in any given scenario.
Supervised learning

As mentioned in a previous article,1 unsupervised learning involves using datasets without clearly noticing the dependent (response) variable. Thus, in principle, all variables are treated in the same way. In supervised learning, 1 variable receives special focus. It is the response that determines the scope of the learning task variable. The response variable is the dependent variable, outcome, explained variable, output, label, or target. In principle, the response is not any different than the other variables.
Superpoint Transformer for 3D Scene Instance Segmentation

Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D instance segmentation framework. 2) Existing method requires a time-consuming intermediate step of aggregation. To address these issues, this paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer. It groups potential features from point clouds into superpoints, and directly predicts instances through query vectors without relying on the results of object detection or semantic segmentation. The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances. Through bipartite matching based on superpoint masks, SPFormer can implement the network training without the intermediate aggregation step, which accelerates the network. Extensive experiments on ScanNetv2 and S3DIS benchmarks verify that our method is concise yet efficient. Notably, SPFormer exceeds compared state-of-the-art methods by 4.3% on ScanNetv2 hidden test set in terms of mAP and keeps fast inference speed (247ms per frame) simultaneously. Code is available at https://github.com/sunjiahao1999/SPFormer.
Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive.
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition

Named entity recognition (NER) is the task to detect and classify entity spans in the text.When entity spans overlap between each other, the task is named as nested NER.Span-based methods have been widely used to tackle nested NER.Most of these methods get a score matrix, where each entry corresponds to a span.However, previous work ignores spatial relations in the score matrix.In this paper, we propose using Convolutional Neural Network (CNN) to model these spatial relations.Despite being simple, experiments in three commonly used nested NER datasets show that our model surpasses several recently proposed methods with the same pre-trained encoders.Further analysis shows that using CNN can help the model find more nested entities.Besides, we find that different papers use different sentence tokenizations for the three nested NER datasets, which will influence the comparison.Thus, we release a pre-processing script to facilitate future comparison.
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL

Relational structures such as schema linking and schema encoding have been validated as a key component to qualitatively translating natural language into SQL queries.However, introducing these structural relations comes with prices: they often result in a specialized model structure, which largely prohibits using large pretrained models in text-to-SQL.To address this problem, we propose RASAT: a Transformer seq2seq architecture augmented with relation-aware self-attention that could leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model effectively.Our model can incorporate almost all types of existing relations in the literature, and in addition, we propose introducing co-reference relations for the multi-turn scenario.Experimental results on three widely used text-to-SQL datasets, covering both singleturn and multi-turn scenarios, have shown that RASAT could achieve state-of-the-art results across all three benchmarks (75.5% EX on Spider, 52.6% IEX on SParC, and 37.4% IEX on CoSQL).
Fine-tuned Language Models are Continual Learners

Recent work on large language models relies on the intuition that most natural language processing tasks can be described via natural language instructions and that models trained on these instructions show strong zero-shot performance on several standard datasets.However, these models even though impressive still perform poorly on a wide range of tasks outside of their respective training and evaluation sets.To address this limitation, we argue that a model should be able to keep extending its knowledge and abilities, without forgetting previous skills.In spite of the limited success of Continual Learning we show that Fine-tuned Language Models can be continual learners.We empirically investigate the reason for this success and conclude that Continual Learning emerges from self-supervision pre-training.Our resulting model Continual-T0 (CT0) is able to learn 8 new diverse language generation tasks, while still maintaining good performance on previous tasks, spanning in total 70 datasets.Finally, we show that CT0 is able to combine instructions in ways it was never trained for, demonstrating some level of instruction compositionality.
DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models

We study the way DALLE-2 maps symbols (words) in the prompt to their references (entities or properties of entities in the generated image).We show that in stark contrast to the way human process language, DALLE-2 does not follow the constraint that each word has a single role in the interpretation, and sometimes re-uses the same symbol for different purposes.We collect a set of stimuli that reflect the phenomenon: we show that DALLE-2 depicts both senses of nouns with multiple senses at once; and that a given word can modify the properties of two distinct entities in the image, or can be depicted as one object and also modify the properties of another object, creating a semantic leakage of properties between entities.Taken together, our study highlights the differences between DALLE-2 and human language processing and opens an avenue for future study on the inductive biases of text-to-image models.
A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7

Traffic sign detection is a crucial task in computer vision, finding wide-ranging applications in intelligent transportation systems, autonomous driving, and traffic safety. However, due to the complexity and variability of traffic environments and the small size of traffic signs, detecting small traffic signs in real-world scenes remains a challenging problem. In order to improve the recognition of road traffic signs, this paper proposes a small object detection algorithm for traffic signs based on the improved YOLOv7. First, the small target detection layer in the neck region was added to augment the detection capability for small traffic sign targets. Simultaneously, the integration of self-attention and convolutional mix modules (ACmix) was applied to the newly added small target detection layer, enabling the capture of additional feature information through the convolutional and self-attention channels within ACmix. Furthermore, the feature extraction capability of the convolution modules was enhanced by replacing the regular convolution modules in the neck layer with omni-dimensional dynamic convolution (ODConv). To further enhance the accuracy of small target detection, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to mitigate the sensitivity to minor positional deviations of small objects. The experimental results on the challenging public dataset TT100K demonstrate that the SANO-YOLOv7 algorithm achieved an 88.7% mAP@0.5, outperforming the baseline model YOLOv7 by 5.3%.
Mutual-Assistance Learning for Object Detection

Object detection is a fundamental yet challenging task in computer vision. Despite the great strides made over recent years, modern detectors may still produce unsatisfactory performance due to certain factors, such as non-universal object features and single regression manner. In this paper, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a robust one-stage detector, referred as MADet, to address these weaknesses. First, the spirit of MA is manifested in the head design of the detector. Decoupled classification and regression features are reintegrated to provide shared offsets, avoiding inconsistency between feature-prediction pairs induced by zero or erroneous offsets. Second, the spirit of MA is captured in the optimization paradigm of the detector. Both anchor-based and anchor-free regression fashions are utilized jointly to boost the capability to retrieve objects with various characteristics, especially for large aspect ratios, occlusion from similar-sized objects, etc. Furthermore, we meticulously devise a quality assessment mechanism to facilitate adaptive sample selection and loss term reweighting. Extensive experiments on standard benchmarks verify the effectiveness of our approach. On MS-COCO, MADet achieves 42.5% AP with vanilla ResNet50 backbone, dramatically surpassing multiple strong baselines and setting a new state of the art. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ShichengMiao16/MADet</uri> .
PEMANFAATAN KERANGKA KERJA INVESTIGASI FORENSIK JARINGAN UNTUK IDENTIFIKASI SERANGAN JARINGAN MENGGUNAKAN SISTEM DETEKSI INTRUSI (IDS)

One of the media to secure computers is to apply Intrusion Detection System (IDS) technology. IDS is an early detection system in the event of a computer network attack. The IDS will alert the computer network administrator in the event of a computer network attack. IDS also records all attempts and activities aimed at disrupting computer networks and other computer network attacks. The purpose of this study is to implement IDS on network systems and analyze IDS logs to determine the types and types of computer network attacks. Logs on the IDS will be analyzed in depth to be used as an effort to improve computer network security. The research method that will be used is applied research. The research was carried out using the Network Forensic Investigation Framework proposed by Pilli, Joshi and Niyogi. The stages of the Network Forensic Investigation Framework are used to perform network simulations, analysis and investigations to determine the types of computer network attacks. The results show that the Network Forensic Investigation Framework facilitates the investigation process when a network attack occurs. The Network Forensic Investigation Framework is effectively used when the computer network has network security support applications such as IDS or others. IDS is effective in detecting network scanning activities and DOS attacks. IDS provides alerts to administrators because there are activities that violate the rules on the IDS.
Measurement-induced state transitions in a superconducting qubit: Within the rotating-wave approximation

Fast nondemolition measurement of superconducting qubits is important for the operation of quantum computers, but readout is constrained by measurement-induced state transitions that shift the qubit population outside of its computational subspace. This work experimentally characterizes the phenomenon and provides an intuitive model to explain its physical process. Surprisingly, the offset charge of the transmon qubit, which is usually ignored, is important in explaining the experiments. These results inform engineering and physics-based solutions to this problem by providing performance limits for the dispersive readout of superconducting qubits.
GPT-RE: In-context Learning for Relation Extraction using Large Language Models

In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3) via in-context learning (ICL), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of ICL for RE: (1) low relevance regarding entity and relation in existing sentence-level demonstration retrieval approaches for ICL; and (2) the lack of explaining input-label mappings of demonstrations leading to poor ICL effectiveness. In this paper, we propose GPT-RE to successfully address the aforementioned issues by (1) incorporating task-aware representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE datasets, and observe that GPT-RE achieves improvements over not only existing GPT-3 baselines, but also fully-supervised baselines as in Figure 1. Specifically, GPT-RE achieves SOTA performances on the Semeval and SciERC datasets, and competitive performances on the TACRED and ACE05 datasets. Additionally, a critical issue of LLMs revealed by previous work, the strong inclination to wrongly classify NULL examples into other pre-defined labels, is substantially alleviated by our method. We show an empirical analysis.
Evaluation of Translation Technology

This collection of scholarly articles asks the question How useful is translation technology? Pointing to the need for a widely used and reliable way to test the efficiency of language translation programs, the presenters show that commercial tools such as translation memories and translation workbenches are popular, and their developers find them useful in terms of productivity, consistency, or quality. However, these claims are rarely proven using objective comparative studies, and this group describes several new statistical approaches to more rigorous evaluation methods.
An Evaluation Of Data Erasing Tools

The permanent removal of data from media is a major area of concern mainly because of the misconception that once a file is deleted or storage media is formatted, it cannot be recovered.There has been the development of both commercial and freeware data erasing tools, which all claim complete file or disk erasure.This report analyzes the efficiency of a number of these tools in performing erasures on an electromechanical drive.It focuses on a selection of popular and modern erasing tools, taking into consideration their usability, claimed erasing standards, and whether they perform complete data erasure with the use of the Write Zero method.
Web-Based Visual Analytics for Social Media

Social media provides a rich source of data that reflects current trends on a multitude of topics. The data can be harvested from Twitter, Facebook, blogs, and other social applications. The high rate of adoption of social media has created a domain that is difficult to analyze, due to the ever-expanding volume of data. Information visualization is key in drawing out features of interest in social media. The Scalable Reasoning System is an application that couples a back-end server equipped with analysis algorithms and an intuitive visual in- terface to allow for investigation. We provide a componentized system that can be rapidly adapted to user needs. The in- formation in which they are most interested is featured prominently in the application. As an example, we have developed a weather and traffic monitoring application for use by emergency operators in the city of Seattle.
Quantum Error-Correcting Codes

Any quantum system inevitably interacts with the environment which causes decoherence. While the environment is generally inaccessible, can we protect our system against noise to maintain its quantum coherence? One technique developed in quantum information science, called the quantum error-correcting codes, does the job. The main idea is to “encode” the system into a subspace of the entire N-qubit space, called the “code space”, such that the errors caused by decoherence of the system can be “corrected”.