Reviewerly/exHarmony huggingface dataset:
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning

Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training. Towards this goal, this paper introduces ExMix (Extreme Mixture): a massive collection of 107 supervised NLP tasks across diverse domains and task-families. Using ExMix, we study the effect of multi-task pre-training at the largest scale to date, and analyze co-training transfer amongst common families of tasks. Through this analysis, we show that manually curating an ideal set of tasks for multi-task pre-training is not straightforward, and that multi-task scaling can vastly improve models on its own. Finally, we propose ExT5: a model pre-trained using a multi-task objective of self-supervised span denoising and supervised ExMix. Via extensive experiments, we show that ExT5 outperforms strong T5 baselines on SuperGLUE, GEM, Rainbow, Closed-Book QA tasks, and several tasks outside of ExMix. ExT5 also significantly improves sample efficiency while pre-training.
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity

State-of-the-art dialogue models still often stumble with regards to factual accuracy and self-contradiction. Anecdotally, they have been observed to fail to maintain character identity throughout discourse; and more specifically, may take on the role of their interlocutor. In this work we formalize and quantify this deficiency, and show experimentally through human evaluations that this is indeed a problem. In contrast, we show that discriminative models trained specifically to recognize who is speaking can perform well; and further, these can be used as automated metrics. Finally, we evaluate a wide variety of mitigation methods, including changes to model architecture, training protocol, and decoding strategy. Our best models reduce mistaken identity issues by nearly 65% according to human annotators, while simultaneously improving engagingness. Despite these results, we find that maintaining character identity still remains a challenging problem.
An Empirical Study on Explanations in Out-of-Domain Settings

Recent work in Natural Language Processing has focused on developing approaches that extract faithful explanations, either via identifying the most important tokens in the input (i.e. post-hoc explanations) or by designing inherently faithful models that first select the most important tokens and then use them to predict the correct label (i.e. select-then-predict models). Currently, these approaches are largely evaluated on in-domain settings. Yet, little is known about how post-hoc explanations and inherently faithful models perform in out-of-domain settings. In this paper, we conduct an extensive empirical study that examines: (1) the out-of-domain faithfulness of post-hoc explanations, generated by five feature attribution methods; and (2) the out-of-domain performance of two inherently faithful models over six datasets. Contrary to our expectations, results show that in many cases out-of-domain post-hoc explanation faithfulness measured by sufficiency and comprehensiveness is higher compared to in-domain. We find this misleading and suggest using a random baseline as a yardstick for evaluating post-hoc explanation faithfulness. Our findings also show that select-then predict models demonstrate comparable predictive performance in out-of-domain settings to full-text trained models.
Models for evaluating the maximum power produced by a Photovoltaic generator

The size of a photovoltaic system up to determine the necessary number of the installed solar panels photovoltaic to adopt a PV system sufficient to meet the needs of the load at any moment and the carrying capacity of the battery, ​it is necessary that the mathematical model used to calculate the power delivered by the solar panel is more practical. In this work, we presented several mathematical models for calculating the maximum power at the output of a photovoltaic module depending of conditions meteorological (Illumination solar and temperature). A comparative study based on Lu Lin 2004 essays was made for the purpose of deducing the most reliable mathematical model, which can be used to calculate the power delivered by a photovoltaic panel.
Canonical quantisation of telegrapher&amp;apos;s equations coupled by ideal nonreciprocal elements

We develop a systematic procedure to quantise canonically Hamiltonians of light-matter models of transmission lines coupled through lumped linear lossless ideal nonreciprocal elements, that break time-reversal symmetry, in a circuit QED set-up. This is achieved through a description of the distributed subsystems in terms of both flux and charge fields. We prove that this apparent redundancy is required for the general derivation of the Hamiltonian for a wider class of networks. By making use of the electromagnetic duality symmetry in transmission lines (waveguides), we provide unambiguous identification of the physical degrees of freedom, separating out the nondynamical parts. This doubled description can also treat the case of other extended lumped interactions in a regular manner that presents no spurious divergences, as we show explicitly in the example of a circulator connected to a Josephson junction through a transmission line. This theory enhances the quantum engineering toolbox to design complex networks with nonreciprocal elements.
Federated Optimization of Smooth Loss Functions

In this work, we study empirical risk minimization (ERM) within a federated learning framework, where a central server minimizes an ERM objective function using training data that is stored across $m$ clients. In this setting, the Federated Averaging (FedAve) algorithm is the staple for determining $\epsilon$-approximate solutions to the ERM problem. Similar to standard optimization algorithms, the convergence analysis of FedAve only relies on smoothness of the loss function in the optimization parameter. However, loss functions are often very smooth in the training data too. To exploit this additional smoothness, we propose the Federated Low Rank Gradient Descent (FedLRGD) algorithm. Since smoothness in data induces an approximate low rank structure on the loss function, our method first performs a few rounds of communication between the server and clients to learn weights that the server can use to approximate clients' gradients. Then, our method solves the ERM problem at the server using inexact gradient descent. To show that FedLRGD can have superior performance to FedAve, we present a notion of federated oracle complexity as a counterpart to canonical oracle complexity. Under some assumptions on the loss function, e.g., strong convexity in parameter, $\eta$-H\"older smoothness in data, etc., we prove that the federated oracle complexity of FedLRGD scales like $\phi m(p/\epsilon)^{\Theta(d/\eta)}$ and that of FedAve scales like $\phi m(p/\epsilon)^{3/4}$ (neglecting sub-dominant factors), where $\phi\gg 1$ is a "communication-to-computation ratio," $p$ is the parameter dimension, and $d$ is the data dimension. Then, we show that when $d$ is small and the loss function is sufficiently smooth in the data, FedLRGD beats FedAve in federated oracle complexity. Finally, in the course of analyzing FedLRGD, we also establish a result on low rank approximation of latent variable models.
Efficient realization of quantum algorithms with qudits

The development of a universal fault-tolerant quantum computer that can solve efficiently various difficult computational problems is an outstanding challenge for science and technology. In this work, we propose a technique for an efficient implementation of quantum algorithms with multilevel quantum systems (qudits). Our method uses a transpilation of a circuit in the standard qubit form, which depends on the parameters of a qudit-based processor, such as their number and the number of accessible levels. This approach provides a qubit-to-qudit mapping and comparison to a standard realization of quantum algorithms highlighting potential advantages of qudits. We provide an explicit scheme of transpiling qubit circuits into sequences of single-qudit and two-qudit gates taken from a particular universal set. We then illustrate our method by considering an example of an efficient implementation of a $6$-qubit quantum algorithm with qudits. We expect that our findings are of relevance for ongoing experiments with noisy intermediate-scale quantum devices that operate with information carrier allowing qudit encodings, such as trapped ions and neutral atoms as well as optical and solid-state systems.
Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

In this work, we aim to solve a practical use-case of unsupervised clustering which has applications in predictive maintenance in the energy operations sector using quantum computers.Using only cloud access to quantum computers, we complete a thorough performance analysis of what some current quantum computing systems are capable of for practical applications involving non-trivial midto-high dimensional datasets.We first benchmark how well distance estimation can be performed using two different metrics based on the swap-test, using angle and amplitude data embedding.Next, for the clustering performance analysis, we generate sets of synthetic data with varying cluster variance and compare simulation to physical hardware results using the two metrics.From the results of this performance analysis, we propose a general, competitive, and parallelized version of quantum k-means clustering to avoid some pitfalls discovered due to noisy hardware and apply the approach to a real energy grid clustering scenario.Using real-world German electricity grid data, we show that the new approach improves the balanced accuracy of the standard quantum k-means clustering by 67.8% with respect to the labeling of the classical algorithm.
Application of Sobolev-Volterra projection and finite element numerical analysis of integral differential equations in modern art design

Abstract The article uses the Spss statistical analysis software to establish a multiple linear regression model of short-term stock price changes in domestic agricultural listed companies. It uses a stable time series based on the ARMA model for stable agricultural value-added, fiscal expenditure and market interest rates. The regression method is used to study its impact on the stock price index. Compared with the existing stock forecasting methods, this method has simple data collection and no specific requirements for data selection, and the prediction results have a high degree of fit. Therefore, this method is suitable for most stocks.
Summary report for the third international competition on computational models of argumentation

Abstract The Third International Competition on Computational Models of Argumentation (ICCMA'19) focused on reasoning tasks in abstract argumentation frameworks. Submitted solvers were tested on a selected collection of benchmark instances, including artificially generated argumentation frameworks and some frameworks formalizing real‐world problems. This competition introduced two main novelties over the two previous editions: the first one is the use of the Docker platform for packaging the participating solvers into virtual “light” containers; the second novelty consists of a new track for dynamic frameworks.
Deterministic controlled bidirectional remote state preparation in dissipative environments

Abstract It is a significant subject to explore effective quantum communication protocol and enhance the efficiency of the transmission process in noisy environments. In this paper, we investigate the bidirectional controlled remote preparation of an arbitrary single-qubit state in the presence of dissipative environments by using two EPR states as the entanglement source. We first construct the quantum circuit of our scheme by means of unitary matrix decomposition procedure, then the effects of the Markovian and non-Markovian environmental noises acting on the EPR states are considered through the analytical derivation and numerical calculations of the corresponding average fidelity. Moreover, we adopt two methods of weak measurement reversal (WMR) and detuning modulation to improve the average fidelity. Our results show that the average fidelity can be remarkably enhanced under appropriate conditions of the WMR strength and the detuning. Compared with the average fidelity behaviors in dissipative environments, it is also shown that the two methods for fidelity improvement are more efficient in the non-Markovian regime than in the Markovian regime.
Error Level Analysis Technique for Identifying JPEG Block Unique Signature for Digital Forensic Analysis

The popularity of unique image compression features of image files opens an interesting research analysis process, given that several digital forensics cases are related to diverse file types. Of interest has been fragmented file carving and recovery which forms a major aspect of digital forensics research on JPEG files. Whilst there exist several challenges, this paper focuses on the challenge of determining the co-existence of JPEG fragments within various file fragment types. Existing works have exhibited a high false-positive rate, therefore rendering the need for manual validation. This study develops a technique that can identify the unique signature of JPEG 8 × 8 blocks using the Error Level Analysis technique, implemented in MATLAB. The experimental result that was conducted with 21 images of JFIF format with 1008 blocks shows the efficacy of the proposed technique. Specifically, the initial results from the experiment show that JPEG 8 × 8 blocks have unique characteristics which can be leveraged for digital forensics. An investigator could, therefore, search for the unique characteristics to identify a JPEG fragment during a digital investigation process.
An IPA of Health Perceptions Among Korean and Japanese Adolescents During the Prolonged COVID-19 Pandemic

This study aims to comparatively analyse the importance and performance of the health of Korean and Japanese adolescents during the prolonged coronavirus disease 2019 (COVID-19) pandemic. Data were collected from 1,341 sampled Korean and Japanese adolescents in September 2021 through online and offline surveys. The collected data were analysed with frequency analysis, reliability testing, t-test, and importance-performance analysis (IPA). The following results were obtained. First, adolescents in the two countries perceive various factors about health as important during the COVID-19 pandemic, but their performance is weak compared to their perceived importance. Second, Korean adolescents had greater perceived importance for all factors of health perception compared to their Japanese counterparts. Third, the difference in performance between Korean and Japanese adolescents was especially evident for &amp;lsquo;hygiene management&amp;rsquo;, and there were significant differences in performance in &amp;lsquo;disease management&amp;rsquo; and &amp;lsquo;physical activity&amp;rsquo;. Fourth, in quadrant 4 of the IPA matrix, there were similarities and differences in a particular factor of health perception between Korean and Japanese adolescents. Based on these results, we proposed measures to emphasise the importance of health and enhance performance among Korean and Japanese adolescents.
LINGUISTIC FEATURES OF FILIPINO NETSPEAK IN ONLINE CONVERSATIONS

This study aimed to identify the linguistic features of Filipino Netspeak, the live experiences of students using it, as well as the educational implications of Netspeak. Hybrid method which involves a combination of discourse analysis and qualitative phenomenological were used. The corpus of the study was composed of a total of 314 Facebook posts, comments, and replies from a Facebook closed group created for seven (7) participants. On the other hand, other data were collected through an in-depth interview with the participants. Results revealed that the linguistic features of Filipino Netspeak include the following: compound, blend, acronym, abbreviation, unique orthography, unconventional use of punctuation marks, and use of emoji. Moreover, themes for students’ experiences in using Filipino Netspeak were the succeeding: easy and comfortable use of shortened words, Netspeak as language of millennial, unintentional use of Netspeak in formal setting, words/emojis learned in online communication, conventional use of Netspeak in social media interaction, miscommunication in Netspeak, and the effect of Netspeak in orthography and grammar. Furthermore, results for educational implications were the following: Netspeak’s popularity in the digital era for student’s faster communication, Netspeak as a form of feedback and communication of teachers to students, Netspeak as a classroom strategy, and Netspeak’s influence in the writing skills of students. KEYWORDS: Netspeak, Filipino netspeak, discourse analysis, phenomenological research, Philippines
Hindsight is 20/20: Retrospective Lessons for Conducting Longitudinal Wearable Sensing Studies

Pervasive sensing using wearables for health monitoring presents a promising and unique opportunity to widely manage illnesses and conditions. To better understand the capabilities and limitations of using wearable devices for health monitoring, systems need to be developed and studies conducted. We conducted one such study for monitoring patients with Chronic Obstructive Pulmonary Disease (COPD), in which we aim to understand the disease and predict patient outcomes. However, despite a carefully well-planned and well-conducted study that resulted in a very large dataset, some non-obvious design oversights meant the data was much less useful. We analyze the shortcomings of our study to construct lessons and concrete actions to avoid these pitfalls. We ratify these lessons by briefly discussing a second iteration of our study, in which we apply these lessons and obtain much better outcomes. Real-world sensing studies are time consuming and expensive investments, for a promising research area. By sharing our failure and proposing actionable lessons, we hope to minimize the risk for others aiming to run such studies.
Provably Confidential Language Modelling

Large language models are shown to memorize privacy information such as social security numbers in training data.Given the sheer scale of the training corpus, it is challenging to screen and filter all privacy data, either manually or automatically.In this paper, we propose Confidentially Redacted Training (CRT), a method to train language generation models while protecting the confidential segments.We borrow ideas from differential privacy (which solves a related but distinct problem) and show that our method is able to provably prevent unintended memorization by randomizing parts of the training process.Moreover, we show that redaction with an approximately correct screening policy amplifies the confidentiality guarantee.We implement the method for both LSTM and GPT language models.Our experimental results show that the models trained by CRT obtain almost the same perplexity while preserving strong confidentiality 1 .SYS: Hello, I am the customer support bot.What do you need?
Innovation of E-Commerce Development Model under the Background of Artificial Intelligence and Wireless Communication

E-commerce refers to the business operation and profit model based on the network environment. Studying the e-commerce model will help to tap the development potential of e-commerce, innovate the content and form of e-commerce, and also help e-commerce companies expand marketing channels and improve sales capabilities. There are many types of e-commerce models, such as BtoB, BtoC, and CtoC, as well as new models such as BtoQ and BtoB. With the development of wireless communications and artificial intelligence, the market scale of e-commerce has gradually expanded, and e-commerce has become a mainstream consumer channel for people. Promoting the development of e-commerce is an issue that e-commerce companies need to pay attention to. This article uses questionnaire surveys and case analysis methods to study e-commerce companies. The results show that the e-commerce market is huge, but the traditional e-commerce model also has a series of problems. It also proposes innovative paths for e-commerce development models, such as logistics acceleration, brand innovation, and payment method innovation, which have important reference significance for the development of e-commerce enterprises. It is suggested that different types of enterprises combine their own comprehensive strength and actual needs to choose e-commerce development strategies suitable for their own development, so as to achieve sustainable development.
A survey on requirements notations in software engineering research

Requirements can be documented using different notations that vary, for example, on expressivity, formality, and visualization. Each of them have advantages and disadvantages on different contexts. In fact, there is not much information on which and how requirements notations are being used, most of all when considering not only the Software Engineering industry, but also its research community. Therefore, this study investigates researchers' applications for requirements notations. Furthermore, we explore their reasoning behind the choice for a notation and how formally these notations are defined. First we reviewed the literature to identify general requirements notations and their definition methods. Then, we conducted a survey study with Software Engineering researchers, asking them about the notations they have used. We received 251 usable responses from 24 countries. Our main findings are that participants' preferred notations are Use Case, Class Diagram, and User Stories; and they choose notations based mainly on their suitability to the problem, their adequacy to the stakeholders, and common usage.
Illustration of a Software-Aided Content Analysis Methodology Applied to Educational Research

This paper presents a software-aided methodology for content analysis by implementing the Leximancer software package, which can convert plain texts into conceptual networks that show how the prevalent concepts are linked with each other. The generated concept maps are associative networks of meaning related to the topics elaborated in the analyzed documents and reflect the creators’ core mental representations. The applicability of Leximancer is demonstrated in an education research context, probing university students’ epistemological beliefs, where a qualitative semantic analysis could be applied by inspecting and interpreting the portrayed relationships among concepts. In addition, concept-map-generating matrices, ensuing from the previous step, are introduced to another specialized software, Gephi, and further network analysis is performed using quantitative measures of centrality, such as degree, betweenness and closeness. Besides illustrating the method of this semantic analysis of textual data and deliberating the advances of digital innovations, the paper discusses theoretical issues underpinning the network analysis, which are related to the complexity theory framework, while building bridges between qualitative and quantitative traditional approaches in educational research.