Reviewerly/exHarmony huggingface dataset:
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations

Geo-tagged images are publicly available in large quantities, whereas labels such as object classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has achieved tremendous success in various natural image and language tasks with limited labeled data. However, existing methods fail to fully leverage geospatial information, which can be paramount to distinguishing objects that are visually similar. To directly leverage the abundant geospatial information associated with images in pre-training, fine-tuning, and inference stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images, which can be transferred to downstream supervised tasks such as image classification. Experiments show that CSP can improve model performance on both iNat2018 and fMoW datasets. Especially, on iNat2018, CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
Early Classifying Multimodal Sequences

Often pieces of information are received sequentially over time. When did one collect enough such pieces to classify? Trading wait time for decision certainty leads to early classification problems that have recently gained attention as a means of adapting classification to more dynamic environments. However, so far results have been limited to unimodal sequences. In this pilot study, we expand into early classifying multimodal sequences by combining existing methods. We show our new method yields experimental AUC advantages of up to 8.7%.
Contrastive Speech Mixup for Low-resource Keyword Spotting

Most of the existing neural-based models for keyword spotting (KWS) in smart devices require thousands of training samples to learn a decent audio representation. However, with the rising demand for smart devices to become more personalized, KWS models need to adapt quickly to smaller user samples. To tackle this challenge, we propose a contrastive speech mixup (CosMix) learning algorithm for low-resource KWS. CosMix introduces an auxiliary contrastive loss to the existing mixup augmentation technique to maximize the relative similarity between the original pre-mixed samples and the augmented samples. The goal is to inject enhancing constraints to guide the model towards simpler but richer content-based speech representations from two augmented views (i.e. noisy mixed and clean pre-mixed utterances). We conduct our experiments on the Google Speech Command dataset, where we trim the size of the training set to as small as 2.5 mins per keyword to simulate a low-resource condition. Our experimental results show a consistent improvement in the performance of multiple models, which exhibits the effectiveness of our method.
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer

Federated Learning (FL), a distributed machine learning technique has recently experienced tremendous growth in popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server communication cost optimization. The ratio of chosen clients and the quantity of local training passes are two hyperparameters that have a significant impact on FL performance. Due to different training preferences across various applications, it can be difficult for FL practitioners to manually select such hyperparameters. In our research paper, we introduce FedAVO, a novel FL algorithm that enhances communication effectiveness by selecting the best hyperparameters leveraging the African Vulture Optimizer (AVO). Our research demonstrates that the communication costs associated with FL operations can be substantially reduced by adopting AVO for FL hyperparameter adjustment. Through extensive evaluations of FedAVO on benchmark datasets, we show that FedAVO achieves significant improvement in terms of model accuracy and communication round, particularly with realistic cases of Non-IID datasets. Our extensive evaluation of the FedAVO algorithm identifies the optimal hyperparameters that are appropriately fitted for the benchmark datasets, eventually increasing global model accuracy by 6% in comparison to the state-of-the-art FL algorithms (such as FedAvg, FedProx, FedPSO, etc.).
AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis

The generation of realistic and contextually relevant co-speech gestures is a challenging yet increasingly important task in the creation of multimodal artificial agents. Prior methods focused on learning a direct correspondence between co-speech gesture representations and produced motions, which created seemingly natural but often unconvincing gestures during human assessment. We present an approach to pre-train partial gesture sequences using a generative adversarial network with a quantization pipeline. The resulting codebook vectors serve as both input and output in our framework, forming the basis for the generation and reconstruction of gestures. By learning the mapping of a latent space representation as opposed to directly mapping it to a vector representation, this framework facilitates the generation of highly realistic and expressive gestures that closely replicate human movement and behavior, while simultaneously avoiding artifacts in the generation process. We evaluate our approach by comparing it with established methods for generating co-speech gestures as well as with existing datasets of human behavior. We also perform an ablation study to assess our findings. The results show that our approach outperforms the current state of the art by a clear margin and is partially indistinguishable from human gesturing. We make our data pipeline and the generation framework publicly available.
SoK: Log Based Transparency Enhancing Technologies

This paper systematizes log based Transparency Enhancing Technologies. Based on work on transparency from multiple disciplines we outline the purpose, usefulness, and pitfalls of transparency. We describe the mechanisms that allow log based transparency enhancing technologies to be implemented, in particular logging mechanisms, sanitisation mechanisms and the trade-offs with privacy, data release and query mechanisms, and how transparency relates to the external mechanisms that enable contesting a system and holding system operators accountable. We illustrate this with two examples, Certificate Transparency and cryptocurrencies, and show the role that transparency plays in their function as well as the issues these systems face in delivering transparency.
Spectral approach to the communication complexity of multi-party key agreement

We propose a linear algebraic method, rooted in the spectral properties of graphs, that can be used to prove lower bounds in communication complexity. Our proof technique effectively marries spectral bounds with information-theoretic inequalities. The key insight is the observation that, in specific settings, even when data sets $X$ and $Y$ are closely correlated and have high mutual information, the owner of $X$ cannot convey a reasonably short message that maintains substantial mutual information with $Y$. In essence, from the perspective of the owner of $Y$, any sufficiently brief message $m=m(X)$ would appear nearly indistinguishable from a random bit sequence. We employ this argument in several problems of communication complexity. Our main result concerns cryptographic protocols. We establish a lower bound for communication complexity of multi-party secret key agreement with unconditional, i.e., information-theoretic security. Specifically, for one-round protocols (simultaneous messages model) of secret key agreement with three participants we obtain an asymptotically tight lower bound. This bound implies optimality of the previously known omniscience communication protocol (this result applies to a non-interactive secret key agreement with three parties and input data sets with an arbitrary symmetric information profile). We consider communication problems in one-shot scenarios when the parties' inputs are not produced by any i.i.d. sources, and there are no ergodicity assumptions on the input data. In this setting, we found it natural to present our results using the framework of Kolmogorov complexity.
Sample-based distance-approximation for subsequence-freeness

In this work, we study the problem of approximating the distance to subsequence-freeness in the sample-based distribution-free model. For a given subsequence (word) $w = w_1 \dots w_k$, a sequence (text) $T = t_1 \dots t_n$ is said to contain $w$ if there exist indices $1 \leq i_1 < \dots < i_k \leq n$ such that $t_{i_{j}} = w_j$ for every $1 \leq j \leq k$. Otherwise, $T$ is $w$-free. Ron and Rosin (ACM TOCT 2022) showed that the number of samples both necessary and sufficient for one-sided error testing of subsequence-freeness in the sample-based distribution-free model is $\Theta(k/\epsilon)$. Denoting by $\Delta(T,w,p)$ the distance of $T$ to $w$-freeness under a distribution $p :[n]\to [0,1]$, we are interested in obtaining an estimate $\widehat{\Delta}$, such that $|\widehat{\Delta} - \Delta(T,w,p)| \leq \delta$ with probability at least $2/3$, for a given distance parameter $\delta$. Our main result is an algorithm whose sample complexity is $\tilde{O}(k^2/\delta^2)$. We first present an algorithm that works when the underlying distribution $p$ is uniform, and then show how it can be modified to work for any (unknown) distribution $p$. We also show that a quadratic dependence on $1/\delta$ is necessary.
Exploring the synergistic potential of quantum annealing and gate model computing for portfolio optimization

Portfolio optimization is one of the most studied problems for demonstrating the near-term applications of quantum computing. However, large-scale problems cannot be solved on today's quantum hardware. In this work, we extend upon a study to use the best of both quantum annealing and gate-based quantum computing systems to enable solving large-scale optimization problems efficiently on the available hardware. The existing work uses a method called Large System Sampling Approximation (LSSA) that involves dividing the large problem into several smaller problems and then combining the multiple solutions to approximate the solution to the original problem. This paper introduces a novel technique to modify the sampling step of LSSA. We divide the portfolio optimization problem into sub-systems of smaller sizes by selecting a diverse set of assets that act as representatives of the entire market and capture the highest correlations among assets. We conduct tests on real-world stock data from the Indian stock market on up to 64 assets. Our experimentation shows that the hybrid approach performs at par with the traditional classical optimization methods with a good approximation ratio. We also demonstrate the effectiveness of our approach on a range of portfolio optimization problems of different sizes. We present the effects of different parameters on the proposed method and compare its performance with the earlier work. Our findings suggest that hybrid annealer-gate quantum computing can be a valuable tool for portfolio managers seeking to optimize their investment portfolios in the near future.
Report from Dagstuhl Seminar 23031: Frontiers of Information Access Experimentation for Research and Education

This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together 37 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) and specifically focused on developing more responsible experimental practices leading to more valid results, both for research as well as for scientific education. The seminar brought together experts from various sub-fields of information access, namely IR, RS, NLP, information science, and human-computer interaction to create a joint understanding of the problems and challenges presented by next generation information access systems, from both the research and the experimentation point of views, to discuss existing solutions and impediments, and to propose next steps to be pursued in the area in order to improve not also our research methods and findings but also the education of the new generation of researchers and developers. The seminar featured a series of long and short talks delivered by participants, who helped in setting a common ground and in letting emerge topics of interest to be explored as the main output of the seminar. This led to the definition of five groups which investigated challenges, opportunities, and next steps in the following areas: reality check, i.e. conducting real-world studies, human-machine-collaborative relevance judgment frameworks, overcoming methodological challenges in information retrieval and recommender systems through awareness and education, results-blind reviewing, and guidance for authors.
Huatuo-26M, a Large-scale Chinese Medical QA Dataset

In this paper, we release a largest ever medical Question Answering (QA) dataset with 26 million QA pairs. We benchmark many existing approaches in our dataset in terms of both retrieval and generation. Experimental results show that the existing models perform far lower than expected and the released dataset is still challenging in the pre-trained language model era. Moreover, we also experimentally show the benefit of the proposed dataset in many aspects: (i) trained models for other QA datasets in a zero-shot fashion; and (ii) as external knowledge for retrieval-augmented generation (RAG); and (iii) improving existing pre-trained language models by using the QA pairs as a pre-training corpus in continued training manner. We believe that this dataset will not only contribute to medical research but also facilitate both the patients and clinical doctors. See \url{https://github.com/FreedomIntelligence/Huatuo-26M}.
THE EFFECTS OF USING CAKE APPLICATION TO IMPROVE LISTENING ABILITY OF THE TENTH GRADE STUDENTS OF STATE SENIOR HIGH SCHOOL 2 OF TUNGKAL JAYA

The objective of the researcher was to find out whether there was any significant effect toward the students who were taught listening using the cake application of the tenth-grade students of state senior high school 2 ofÂ Tungkal Jaya.Â The population in this study was all class X for the academic year 2021/2022 . The total population was 153 students from 5 classes . The sample consists of students taken from classes X MIA 1 and X MIA 3 using purposive sampling . A quasi - Experimental design with Nanequvalent control group design pre - test and post - test was used in this study . In collecting the data , the researcher used a written test , and then the data was analyzed using normality , homogeneity , and a t - test . The homogeneity test score for the pre-test and post-test experiments was 0.214. The normality test was more significant than 0.05, Furthermore , the calculation result of the compatibility t - test was 2,322 , which was higher than the critical value of t - table 1.71, The null hypothesis (Ho) was rejected, while the alternative hypothesis (Ha) was accepted. Meaning that there was a significant effect toward the students who taught listening using the cake application of Tungkal Jaya and those who were not.
NEARPOD WEBSITE STRATEGY IN INCREASING STUDENTS' LISTENING ABILITY

The main problem of this study was " is there any significant effect on the students' listening achievement after being taught by using Nearpod and those who are not?. The method used in this study was quantitative or quasi-experimental method. The data collected by using written test and online based test on Nearpod Website. The data were analyzed through matched t-test formula and percentage of analysis to find out the effectiveness of teaching listening through Nearpod and Conventional Method. It was found out that the value of t-obtained was 8.12 where the value of the t-table at 0.05 significant level for two tailed with 18 (df) was 2.100. It means that there was significant difference between the result of pre-test and post-test especially the experimental class. It can be stated that the null hypothesis (Ho) was rejected and the alternative hypothesis (Ha) was accepted. It is indicated that Nearpod Website Strategy could increase studentsâ€™ listening ability.
READING HABITS IN THE DIGITAL AGE: CHANGES TREND AND BEHAVIOR IN ALPHA GENERATION

This thesis was concerned with the Reading Habits In The Digital Age: Changes Trend and Behavior in Alpha Generation. The problem of this research was how could trends and behaviors in alpha generation change reading habits. The purpose of this study is to identify whether the alpha generation has the habit of reading in the digital age. The objective of this research was generation alpha especially students of Junior High School 11 Palembang, Seventh-grade. Based on the data taken from the students choose the answer based on their honest opinion. There were (13%) Fully Agree and (50%) Agree of 30 students were agree on reading habits and the use of technology for reading habits. And (4%) Highly Disagree and (33%) Disagree about reading habits and the use of technology so here the comparison was quite comparable to the reading habits of students using technology in this digital age. It could be can be concluded that the alpha generation has a habit of reading using technology that is included in the digital age such as using a handphone or laptop and reading habits have reading behavior in students.
JOURNALIST QUESTIONS TECHNIQUE IN WIRITING ABILITY WITH NARRATIVE TEXT

The purpose of the study was to describe how the students use the journalist questions technique in writing ability in narrative text and to describe the studentsâ€™ responses produced in the class, the researcher would use written responses. This study used qualitative research, related to collect and analyse the data in the various forms, especially non-numeric forms. The researcher chosed this approach because the study's objectives include demonstrating knowledge of detailed and in-depth sample evaluation as well as class XI students at Senior High School PGRI 2 Palembang. The result showed that the narrative text was an idea from the students to understand and how to use journalist questions in writing with narrative text. The result showed that the 11th graders have a positive response towards the use of journalist questions in writing ability with narrative text. As a results of the studentsâ€™ responses produced in the class, the researcher found that students showed a range of behaviors that demonstrated their writing ability in narrative text and evaluation of the texts as well as of their responses. The class had a relatively sufficient feedback which provided. Students demonstrated a high comprehension from the text. The amounts of tasks in the text clearly aimed to reach the goal. Studentsâ€™ responses in written form also showed that they were trained to be more capable in writing.
IMPROVING STUDENTS' MOTIVATION AND SPEAKING ABILITY USING ONLINE MEDIA LEARNING

The objective of this study was to find out and analyze the process of improving studentsâ€™ motivation and speaking ability of the third year students package C of SPNF SKB Kab,. OKU by using WhatsApp Application. The method which used in this research is classroom action research where the researcher works together with the collaborator/other English teacher. This research was carried out by following Kemmis &amp; Taggart action research procedure where it took place in 3 cycles. The result of this study found that there was improvement of students' speaking ability and motivation. The score of minimum completeness criteria of English lesson was 70. Finding in cycle 1 there were 15 students who pass the minimum completeness criteria or 50 % of students. Finding test in cycle 2 there were 20 students who pass the minimum completeness criteria or 66% of students and than the finding in cycle 3 there were 26 students who pass the minimum completeness criteria or 86% of students. There were improvement of students speaking ability from cycle 1 to cycle 2 gained improvement as 16% and from cycle 2 to cycle 3 gained improvement as 20%. The students' participation increased during the teaching learning process whereas it indicates the students' motivation had increased. Utami (2020) have researched about WhatsApp Application in blended learning where she found that WhatsApp application effective to use as a learning media in writing descriptive text and now the researcher found that WhatsApp application can use as a learning media to improve studentsâ€™ motivation and students' speaking ability.
THE IMPLEMENTATION OF MOBILE LEGEND GAME TO INCREASE VOCABULARY AND LEARNING ACTIVENESS OF FIFTH GRADE STUDENTS AT INSAN MANDIRI CENDEKIA PALEMBANG

The objective of this study was to increasing vocabulary and learning activeness of fifth grade students at Insan Mandiri Cendekia Palembang. The method which used in this study is classroom action research where it took place in 2 cycles. The result of this study found that there was improvement of students' vocabulary and learning activeness. The score of minimum completeness criteria of English lesson was 70. In vocabulary test cycle 1, there were 16students who passed the minimum completeness criteria or 49% of students. And in vocabulary test cycle 2, there were 29 students who passed the minimum completeness criteria or 89% of students. There was improvement of student's vocabulary as 40%where from cycle 1 to cycle 2. The studentsâ€™ learning activeness increased during the teaching learning process whereas it indicated the studentsâ€™ learning activeness had increased.
PERCEPTIONS ON THE USE OF PLATFORM IN TEACHING AND LEARNING ENGLISH IN PANDEMIC ERA AT GRADE XII OF SMKN IN OKU

The objective of this research was to determine the perceptions of teachers, students, and parents in using the Platform for Teaching and Learning English during the pandemic. This research is qualitative research with a qualitative description method. This research was carried out in SMK 1, 2, and 3 OKU with respondents being all English teachers, students, and parents of grade XII. Data collection techniques include observation techniques, questionnaires, and interviews. The results of the study show that the Platform (Google Classroom, Telegram, and Whatsapp) for teachers is very helpful as a medium for delivering materials, but assignments sent by students are sometimes still late, for students, as well, this media is helpful for them in learning during the pandemic, but sometimes accessing materials or collecting assignments is still difficult because the network is not stable and sometimes they don't have a quota, and for parents, this media is very helpful for their children to learn, but sometimes the network is not stable, so that it makes it difficult for them to access materials and collect their assignments as well as economic factors that make it difficult for them to buy internet quota.