Reviewerly/exHarmony huggingface dataset:
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors

Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learning novel classes given only a few samples. While competitive results have been achieved using two-stage FSOD detectors, typically one-stage FSODs underperform compared to them. We make the observation that the large gap in performance between two-stage and one-stage FSODs are mainly due to their weak discriminability, which is explained by a small post-fusion receptive field and a small number of foreground samples in the loss function. To address these limitations, we propose the Few-shot RetinaNet (FSRN) that consists of: a multi-way support training strategy to augment the number of foreground samples for dense meta-detectors, an early multi-level feature fusion providing a wide receptive field that covers the whole anchor area and two augmentation techniques on query and source images to enhance transferability. Extensive experiments show that the proposed approach addresses the limitations and boosts both discriminability and transferability. FSRN is almost two times faster than two-stage FSODs while remaining competitive in accuracy, and it outperforms the state-of-the-art of one-stage meta-detectors and also some two-stage FSODs on the MS-COCO and PASCAL VOC benchmarks.
An adaptive image restoration algorithm based on hybrid total variation regularization

In imaging systems, the mixed Poisson-Gaussian noise (MPGN) model can accurately describe the noise present.Total variation (TV) regularization-based methods have been widely utilized for Poisson-Gaussian removal with edge-preserving.However, TV regularization sometimes causes staircase artifacts with piecewise constants.To overcome this issue, we propose a new model in which the regularization term is represented by a combination of total variation and high-order total variation.We study the existence and uniqueness of the minimizer for the considered model.Numerically, the minimization problem can be efficiently solved by the alternating minimization method.Furthermore, we give rigorous convergence analyses of our algorithm.Experiments results are provided to demonstrate the superiority of our proposed hybrid model and algorithm for deblurring and denoising images simultaneously, in comparison with several state-of-the-art numerical algorithms.
Lsarn: Long-Short-Attention Recurrent Networks for Session-Based Recommendation

A session-based recommendation system recommends the next item in an anonymous sequence. With high uncertainty of user behavior and ultra-long sequences, it is difficult to predict the next user’s behavior. To reduce the loss of long-distance information transmission in ultra-long sequence capture, this study constructs a long- and short-term attention recommendation mode, including activation units and two time gates. An activation unit is designed to calculate user short-term interest similarity scores, and a time-GRU with two time gates is designed. A time signal is introduced to capture the ultra-long sequence of a user’s behavior, and a self-learning attention layer is designed to further extract the user’s long-term interest. The long- and short-term interest are merged, and cross-entropy is used as the objective function to calculate the click probability distribution. An experimental study on the Yoochoose1/64 and Diginetica datasets shows that compared with baseline methods, the proposed model increases the recall metric by 5.30% and 19.58%, and the MRR evaluation metric by 9.63% and 29.85%, respectively. This study also verifies the effectiveness of the time-GRU and activation unit, and the influence of the session sequence length on recommendations.
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking

Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets. This pipeline covers scenarios like question answering or navigational queries, however, for information-seeking scenarios, users often provide information on whether a document is relevant to their query in form of clicks or explicit feedback. Therefore, in this work, we explore how relevance feedback can be directly integrated into neural re-ranking models by adopting few-shot and parameter-efficient learning techniques. Specifically, we introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant. Further, we explore Cross-Encoder models that we pre-train using meta-learning and subsequently fine-tune for each query, training only on the feedback documents. To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario. Extensive experiments demonstrate that integrating relevance feedback directly in neural re-ranking models improves their performance, and fusing lexical ranking with our best performing neural re-ranker outperforms all other methods by 5.2 nDCG@20.
System Design and Implementation of Online Admission System at XYZ University

Digital transformation that is developing along with the industrial revolution 4.0 makes all sectors must be able to adapt and compete more competitively. XYZ University in Ambon, Maluku is currently still carrying out the admissions process manually and using the Google Form. This study aims to design and implement a web-based admissions system at XYZ University. Thus, this system can help the admissions committee and the new students who will register from anywhere and at any time. The method used in this research is Agile Software Development (ASD). This method is very popular in the IT industry today because of its speed and ability to adapt to the changing needs of users. ASD has several models and the one used in this study is the Scrum model, where each Product Backlog obtained from the user is divided into several sprints to make software development easier and faster. The results of this study have been tested and are running well from an operational and functional standpoint to help the admissions committee and new students in the registration process. The conclusions based on the results of this study are (1) the ASD method can help and speed up the software development process, and can be adapted to user needs because it is end-user-centered; (2) This system can help solve research problems, in this case the admissions committee at XYZ University to manage online admissions of new students. In addition, this system can make the process of admitting new students easier and more affordable for students to register from anywhere and at any time.
Visual Image Recognition of Basketball Turning and Dribbling Based on Feature Extraction

The processing of basketball videos with complex contents faces several challenges in terms of global motion features, group motion features, and individual pose features. The current research cannot solve problems, such as the diverse spatiotemporal features of actions, the utilization of correspondence between spatiotemporal features, the increase of data volume, and the complexity of the network. To solve these problems, this paper studies the visual image recognition of basketball turning and dribbling based on feature extraction. Specifically, the optical flow image was introduced to establish the relationship between the velocity field of the basketball turning and dribbling and the grayscale of the image frame, such as to effectively depict the time variation of pixels. In addition, a convolutional neural network was established based on multi-feature learning to process the sports video image frames, and to extract more spatiotemporal features of basketball turning and dribbling. To improve the feature utilization of the action recognition model, this paper strengthens the extraction of dynamic and static features for the recognition of the player's basketball turning and dribbling in the same scene, and improves the existing convolutional neural network. Furthermore, the multi-feature learning of motion excitation and temporal aggregation of actions were completed. The proposed model was proved effective through experiments.
Urgency of digitizing school libraries in Indonesia’s post-truth era: A cross-perspective review

The pandemic era has changed all aspects of human activity to be online-based, including the learning process. The online learning process requires digital-based learning resources and knowledge. The condition of school libraries in Indonesia, which is still in the library automation system stage, causes the library's role as a center of knowledge to be not optimal. Therefore, this study examined the urgency of digitizing school libraries to create learning centers based on various perspectives. This study used a qualitative approach with a literature review type. The validity and reliability test used used cross-reference source checks, quick recording, member checks, and extended observation. Data analysis used content analysis with the Miles and Huberman approach model. Based on legal, psychological, health, educational, economic, social, and technological perspectives, research showed the urgency of digitizing school libraries in the post-truth era. Library digitalization has created an educational space allowing the learning process in the library through the provision of collection resources in digital collections by the library. Schools face obstacles in digitizing libraries: inadequate facilities and infrastructure, lack of skills and abilities of librarians for digitization, lack of support from schools as institutions that oversee libraries, and lack of funds provided. Libraries need to consider barriers and solutions. If realized, the library can become a knowledge center as the primary source of information providing the required information, especially for school-age users.
Literasi informasi remaja pengguna perpustakaan di era pandemi Covid-19

Information literacy is one of the essential skills that society must possess, especially teenagers as students who need information for learning and as part of society who need information in general. It is expected that teenagers can choose and select the information they need from these diverse needs. The Covid-19 pandemic, which has hampered several activities, has limited the youth in developing information literacy skills, both through the library as a service provider of the development of information literacy and digitally, which still needs to be fully understood by them. This study aimed to find out information literacy of young library users during the Covid-19 pandemic era, starting from what programs were appropriate and could be given to students, especially those produced by libraries, and to find out the impact they had on teenagers. This study used a literature review with a type of narrative literature review. The program that the library could provide to students under normal circumstances or during the current pandemic was a literacy movement that would make them accustomed to interacting with various kinds of information, one of which was related to the Covid-19 pandemic. The impact of the literacy movement program in the library is that young library users can have information literacy skills, making them more information literate both for general knowledge and for learning as students.
Classifiying The Factors Influencing The Human Development Index in Riau Province using Principal Component Analysis

The Human Development Index is a critical indicator of economic growth. Several factors, including average length of schooling (X1), expected length of schooling (X2), life expectancy at birth (X3), number of health workers (X4), number of health facilities (X5), spending per capita (X6), open unemployment rate (X7), number of poor people (X8), percentage of households with proper drinking water sources (X9), and GRDP growth rate (X10), can influence the Human Development Index. The purpose of this research was to simplify the factors that influence the human development index in Riau Province in 2021. Data analysis used R-Studio software by applying descriptive statistical analysis, Principal Component analysis, and Biplot analysis. The analysis revealed that the ten variables that influence human development index in Riau in 2021 can be divided into three categories: community service quality, health facilities, access, and economic conditions. These three factors can describe up to 80% of the diversity of the data.
General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation

We investigate unbiased high-dimensional mean estimators in differential privacy. We consider differentially private mechanisms whose expected output equals the mean of the input dataset, for every dataset drawn from a fixed bounded $d$-dimensional domain $K$. A classical approach to private mean estimation is to compute the true mean and add unbiased, but possibly correlated, Gaussian noise to it. In the first part of this paper, we study the optimal error achievable by a Gaussian noise mechanism for a given domain $K$ when the error is measured in the $\ell_p$ norm for some $p \ge 2$. We give algorithms that compute the optimal covariance for the Gaussian noise for a given $K$ under suitable assumptions, and prove a number of nice geometric properties of the optimal error. These results generalize the theory of factorization mechanisms from domains $K$ that are symmetric and finite (or, equivalently, symmetric polytopes) to arbitrary bounded domains. In the second part of the paper we show that Gaussian noise mechanisms achieve nearly optimal error among all private unbiased mean estimation mechanisms in a very strong sense. In particular, for every input dataset, an unbiased mean estimator satisfying concentrated differential privacy introduces approximately at least as much error as the best Gaussian noise mechanism. We extend this result to local differential privacy, and to approximate differential privacy, but for the latter the error lower bound holds either for a dataset or for a neighboring dataset, and this relaxation is necessary.
Learning in POMDPs is Sample-Efficient with Hindsight Observability

POMDPs capture a broad class of decision making problems, but hardness results suggest that learning is intractable even in simple settings due to the inherent partial observability. However, in many realistic problems, more information is either revealed or can be computed during some point of the learning process. Motivated by diverse applications ranging from robotics to data center scheduling, we formulate a Hindsight Observable Markov Decision Process (HOMDP) as a POMDP where the latent states are revealed to the learner in hindsight and only during training. We introduce new algorithms for the tabular and function approximation settings that are provably sample-efficient with hindsight observability, even in POMDPs that would otherwise be statistically intractable. We give a lower bound showing that the tabular algorithm is optimal in its dependence on latent state and observation cardinalities.
Multidimensional Quantum Fourier Transformation

The Quantum Fourier Transformation (QFT) is a well-known subroutine for algorithms on qubit-based universal quantum computers. In this work, the known QFT circuit is used to derive an efficient circuit for the multidimensional QFT. The complexity of the algorithm is $\mathcal{O}( \log^2(M)/d )$ for an array with $M=(2^n)^d$ elements $(n \in \mathbb{N})$ equally separated along $d$ dimensions. Relevant properties for application are discussed. An example on current hardware is depicted by a 6 qubit 2D-QFT with an IBM quantum computer.
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions

Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions. With proper abstractions, solutions to hard problems can be expressed concisely, thus making them more likely to be found. In this paper, we propose Learning Mathematical Abstractions (LEMMA): an algorithm that implements this idea for reinforcement learning agents in mathematical domains. LEMMA augments Expert Iteration with an abstraction step, where solutions found so far are revisited and rewritten in terms of new higher-level actions, which then become available to solve new problems. We evaluate LEMMA on two mathematical reasoning tasks--equation solving and fraction simplification--in a step-by-step fashion. In these two domains, LEMMA improves the ability of an existing agent, both solving more problems and generalizing more effectively to harder problems than those seen during training.
Fair contrastive pre-training for geographic image segmentation

Contrastive self-supervised learning is widely employed in visual recognition for geographic image data (remote or proximal sensing), but because of landscape heterogeneity, models can show disparate performance across spatial units. In this work, we consider fairness risks in such contrastive pre-training; we show learnt representations present large performance gaps across selected sensitive groups: urban and rural areas for satellite images and city GDP level for street view images on downstream semantic segmentation. We propose fair dense representations with contrastive learning (FairDCL) to address the issue, a multi-level latent space de-biasing objective, using a novel dense sensitive attribute encoding technique to constrain spurious local information disparately distributes across groups. The method achieves improved downstream task fairness and outperforms state-of-the-art methods for the absence of a fairness-accuracy trade-off. Image embedding evaluation and ablation studies further demonstrate effectiveness of FairDCL. As fairness in geographic imagery is a nascent topic without existing state-of-the-art data or results, our work motivates researchers to consider fairness metrics in such applications, especially reinforced by our results showing no accuracy degradation. Our code is available at: https://anonymous.4open.science/r/FairDCL-1283
News Recommendation with User Local-Activation Modeling and News Heterogeneity Modeling

News recommendation is widely used in industry, and it is very important to improve user experience and alleviate data overload. User and news representation learning are two key tasks of news recommendation. Most of the existing news recommendation methods take the click history sequence as a whole to construct the user interest representation, but these methods lack the consideration of the user reading habits, and it is difficult to capture user’s multiple potential interest. Furthermore, they usually encode news titles and news content using deep neural networks with the same structure, without considering the heterogeneity among them, which may not be sufficient. In this work, we propose a news recommendation method with user local-activation modeling (ULM) and news heterogeneity modeling (NHM). ULM uses CNN to slice the click history sequence, and then learn fine-grained local interest representations for different slices respectively, finally use candidate news to activate key local interests. NHM uses self-attention network and bidirectional GRU network to encode news titles and contents respectively, and fuses them into news representation through attention mechanism. A large number of experiments on MIND dataset have proved the effectiveness of our method.
El uso de software educativo en el desarrollo de las destrezas lógicas en la educación superior

In certain occasions we wonder why students fail to solve certain types of logic problems and not in others, one of the causes of this fact is due to the lack of knowledge of some learning technique or because they have poor logical reasoning, which is reflected in the low academic performance of students. The population considered is the Information Technology Career of the Peninsula Santa Elena State University and as a sample two parallels are taken from the population that are linked to this study. The investigated topic is of vital importance in subjects where analysis and logical reasoning of problems are required, in addition, it directly influences the practice of basic thought processes.
Aprendizaje de Lectoescritura en la Pandemia Covid-19 y Métodos de Intervención

This paper identifies through social mapping a specific problem observed and intervened through participatory action research related to the learning processes in reading and writing of children in second grade of an educational institution. The main objective was to understand the academic strategies and tools used by families during the COVID 19 pandemic and to document the intervention methods to address the problems found in the territory under study. A bibliographic review was conducted in indexed databases, in order to document the theories, categories and variables that make up the subject under study, in order to implement participatory action research within social mapping and ways to implement in the classroom the educational methods explored. &nbsp;&nbsp;Within the results, the lack of knowledge on the part of the guardians and parents in teaching-learning strategies from home through a technological medium is evidenced. As a main conclusion, the process of identification and analysis of results allows the development of more viable and didactic pedagogical strategies to be used in the classroom and by parents at home in order to strengthen students' reading and writing skills.
Potencial pedagógico motivacional de las Tecnologías de Información y Comunicación en la educación básica secundaria: Alternativa para fortalecer la atención – concentración en tiempos de postpandemia

This article presents the results of a study entitled Strengthening attention-concentration in high school students through the use of ICT. Its purpose is to show the pedagogical-motivational potential of these tools linked to attention-concentration, based on an action-research experience where attention-concentration problems were diagnosed, which were intervened through the application of three (3) workshops to an intentional sample of ten (10) students belonging to the IED Mayor de Barranquilla and the Caribbean, Colombia. The results revealed that, once the interests of the group were aligned with the pedagogical purposes of technological mediation, the students improved their attention - concentration; they were able to be enthusiastic, participate in a disciplined manner; produce and socialize content on websites and social networks with reflective messages alluding to the importance of ICT as a tool favorable to the consolidation of this skill. It is concluded that ICTs are an effective tool to consolidate attention-concentration as a determining skill in student academic performance.
USING LIVE BROADCASTING ON FACEBOOK AMONG JORDANIAN JOURNALISTS

Journalists' use of live broadcasting on social media platforms has become popular to share news and discuss it with followers. This study aimed to know what the gratifications achieved from the use of the live broadcast feature on Facebook among Jordanian journalists and what are the most posts of journalists in the live broadcast via Facebook, which received great interaction from their viewers. To achieve the study objectives, descriptive research has used by electronic questionnaire. The study sample consisted of (114) journalists using live broadcasting, selected purposely. The results of the study revealed that the use of live broadcasting significantly contributed to increasing journalist skills. On the other hand, Live broadcasting which broadcasts exclusive news ranked first for the most viewed posts.