Reviewerly/exHarmony huggingface dataset:
Robust Federated Learning With Noisy Labels

Federated learning is a paradigm that enables local devices to jointly train a server model while keeping the data decentralized and private. In federated learning, since local data are collected by clients, it is hardly guaranteed that the data are correctly annotated. Although a lot of studies have been conducted to train the networks robust to these noisy data in a centralized setting, these algorithms still suffer from noisy labels in federated learning. Compared to the centralized setting, clients' data can have different noise distributions due to variations in their labeling systems or background knowledge of users. As a result, local models form inconsistent decision boundaries and their weights severely diverge from each other, which are serious problems in federated learning. To solve these problems, we introduce a novel federated learning scheme that the server cooperates with local models to maintain consistent decision boundaries by interchanging class-wise centroids. These centroids are central features of local data on each device, which are aligned by the server every communication round. Updating local models with the aligned centroids helps to form consistent decision boundaries among local models, although the noise distributions in clients' data are different from each other. To improve local model performance, we introduce a novel approach to select confident samples that are used for updating the model with given labels. Furthermore, we propose a global-guided pseudo-labeling method to update labels of unconfident samples by exploiting the global model. Our experimental results on the noisy CIFAR-10 dataset and the Clothing1M dataset show that our approach is noticeably effective in federated learning with noisy labels.
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.
Knowledge Graphs: Opportunities and Challenges

Abstract With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.
Active Learning for Deep Visual Tracking

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restrictive in practice, because manually labeling such a large number of training samples is time-consuming and prohibitively expensive. In this article, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNN model. Under the guidance of active learning, the tracker based on the trained deep CNN model can achieve competitive tracking performance while reducing the labeling cost. More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multiframe collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest-neighbor discrimination method based on the average nearest-neighbor distance to screen isolated samples and low-quality samples. Therefore, the training samples’ subset selected based on our method requires only a given budget to maintain the diversity and representativeness of the entire sample set. Furthermore, we adopt a Tversky loss to improve the bounding box estimation of our tracker, which can ensure that the tracker achieves more accurate target states. Extensive experimental results confirm that our active-learning-based tracker (ALT) achieves competitive tracking accuracy and speed compared with state-of-the-art trackers on the seven most challenging evaluation benchmarks. Project website: https://sites.google.com/view/altrack/.
Talking health: trusted health messengers and effective ways of delivering health messages for rural mothers in Southwest Ethiopia

Access to trusted health information has contribution to improve maternal and child health outcomes. However, limited research to date has explored the perceptions of communities regarding credible messenger and messaging in rural Ethiopia. Therefore, this study aimed to explore sources of trusted maternal health information and preferences for the mode of delivery of health information in Jimma Zone, Ethiopia; to inform safe motherhood implementation research project interventions.An exploratory qualitative study was conducted in three districts of Jimma Zone, southwest of Ethiopia, in 2016. Twelve focus group discussions (FGDs) and twenty-four in-depth interviews (IDIs) were conducted among purposively selected study participants. FGDs and IDIs were conducted in the local language, and digital voice recordings were transcribed into English. All transcripts were read comprehensively, and a code book was developed to guide thematic analysis. Data were analyzed using Atlas.7.0.71 software.Study Participants identified as Health Extension Workers (HEWs) and Health Development Army (HDA) as trusted health messengers. Regarding communication channels, participants primarily favored face-to-face/interpersonal communication channels, followed by mass media and traditional approaches like community conversation, traditional songs and role play.In particular, the HEW home-to-home outreach program for health communication helped them to build trusting relationships with community members; However, HEWs felt the program was not adequately supported by the government.Health knowledge transfer success depends on trusted messengers and adaptable modes. The findings of this study suggest that HEWs are a credible messenger for health messaging in rural Ethiopia, especially when using an interpersonal message delivery approach. Therefore, government initiatives should strengthen the existing health extension packages by providing in-service and refresher training to health extension workers.
The Current Issues of Indonesian EFL Students’ Writing Skills: Ability, Problem, and Reason in Writing Comparison and Contrast Essay

The aim of this study is to investigate issues of Indonesian EFL students’ writing skills, covering ability, problem and reason why they got problems in writing comparison and contrast essay. The convergent parallel of mixed methods was used to explore these issues by involving 52 EFL students of IAIN Samarinda who enrolled the academic essay writing course. The three research instruments; essay writing test, open-ended questionnaire and interview were used in the data collection. The findings of this study revealed that the Indonesian EFL students’ writing ability was good that categorized with the mean score 77.83. However, some of them encounter problems in writing aspects including content, organization, vocabulary, grammar, and mechanics. Their reasons in experiencing these problems are not only limited knowledge of writing aspects and comparison and contrast essay itself, but also they had own personal reasons; lack of writing practice, writing dislike, writing anxiety, negative writing perception, low writing motivation, insufficient time given in writing test, and also inadequate teaching writing process taught by their lecturers. Indeed, the teaching writing improvement that involved EFL students and lecturers should be well-integrated in order to alleviate the students’ essay writing problems related to their reasons and to improve their writing ability. Factors caused the writing difficulties will be discussed further and some suggestions will be given on this research.
Learning with fuzzy hypergraphs: A topical approach to query-oriented text summarization

Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph in which edges depict relationships of lexical similarity between sentences. This approach fails to capture semantic similarities between sentences when they express a similar information but have few words in common and are thus lexically dissimilar. To overcome this issue, we propose to extract semantic similarities based on topical representations of sentences. Inspired by the Hierarchical Dirichlet Process, we propose a topic model to infer topic distributions of sentences. As each topic defines a semantic connection among sentences with a certain degree of membership for each sentence, we propose a fuzzy hypergraph model in which nodes are sentences and fuzzy hyperedges are topics. To produce an informative summary, we extract a set of sentences from the corpus by simultaneously maximizing their relevance to a user-defined query, their centrality in the fuzzy hypergraph and their coverage of topics present in the corpus. We formulate an algorithm building on the theory of submodular functions to solve the associated optimization problem. A thorough comparative analysis with other graph-based summarizers demonstrates the superiority of our method in terms of content coverage of the summaries.
Quantum Optics of Spin Waves through ac Stark Modulation

We bring the set of linear quantum operations, important for many fundamental studies in photonic systems, to the material domain of collective excitations known as spin waves. Using the ac Stark effect we realize quantum operations on single excitations and demonstrate a spin-wave analog of the Hong-Ou-Mandel effect, realized via a beam splitter implemented in the spin-wave domain. Our scheme equips atomic-ensemble-based quantum repeaters with quantum information processing capability and can be readily brought to other physical systems, such as doped crystals or room-temperature atomic ensembles.
Neural Session-Aware Recommendation

Recommender systems help users find items they are likely to interact within the near future, such as products to buy in e-commerce or songs to play in music websites. The Traditional recommendation methods make predictions based on long-term user profiles, i.e., the items a user interacted with in the past while ignoring the time and order of the interactions. Recent findings, however, suggest that users may exhibit interest to items in a certain order depending on situations and more recent items in a sequence have a larger impact on the subsequent choices. Moreover, in many practical applications, user-item interactions are organized into short sessions, where each session reflects the user's short-term interest in addition to long-term preferences. Leveraging both long-term user profiles and short-term sequential patterns from sessions can lead to more accurate models known as the session-aware recommendation methods. In this paper, we explore various strategies to integrate user long-term preferences with session patterns encoded by recurrent neural networks (RNNs). The strategies include integrating user embeddings with input and output of session RNNs, integrating with fixed or adaptive contributions of the user and session components by using a specially designed gating mechanism. We conducted an empirical evaluation of three publicly available datasets. The results indicate that combining user long-term profiles with the output of session RNNs yields improved predictions and the proposed adaptive integration model outperforms the state-of-the-art sequential and session-aware recommendation methods.
Instagram: A Platform to Develop Student’s Writing Ability

Traditional method of teaching writing has been seen monotonous to the current generation thus this forces educators to incorporate and integrate Web 2.0 which allows for more exposure to teaching and learning as it involves the use of technology and the internet. This study explores the use of Instagram as a platform in developing students’ English language writing ability and to discover their attitudes towards Instagram as an interactive learning tool. Employing an action research design, 25 Form 3 English language learners in SM Sains Rembau were involved and their posts containing descriptive writing were given corrective feedback and analyzed. A survey was conducted to collect their attitude. The results of the study indicate Instagram as an effective tool for developing learners’ vocabulary range and grammatical accuracy. Learners also portrayed positive attitude as they fancied the use of Instagram for writing. Instagram can be an effective pedagogical tool in second language acquisition if used and supervised appropriately.
Differentially Private Fair Learning

Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. We design two learning algorithms that simultaneously promise differential privacy and equalized odds, a 'fairness' condition that corresponds to equalizing false positive and negative rates across protected groups. Our first algorithm is a private implementation of the equalized odds post-processing approach of [Hardt et al., 2016]. This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of 'disparate treatment'. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of [Agarwal et al., 2018] that can be used to find the optimal fair classifier, given access to a subroutine that can solve the original (not necessarily fair) learning problem. This algorithm is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.
Modular verification for almost-sure termination of probabilistic programs

In this work, we consider the almost-sure termination problem for probabilistic programs that asks whether a given probabilistic program terminates with probability 1. Scalable approaches for program analysis often rely on modularity as their theoretical basis. In non-probabilistic programs, the classical variant rule (V-rule) of Floyd-Hoare logic provides the foundation for modular analysis. Extension of this rule to almost-sure termination of probabilistic programs is quite tricky, and a probabilistic variant was proposed by Fioriti and Hermanns in POPL 2015. While the proposed probabilistic variant cautiously addresses the key issue of integrability, we show that the proposed modular rule is still not sound for almost-sure termination of probabilistic programs. Besides establishing unsoundness of the previous rule, our contributions are as follows: First, we present a sound modular rule for almost-sure termination of probabilistic programs. Our approach is based on a novel notion of descent supermartingales. Second, for algorithmic approaches, we consider descent supermartingales that are linear and show that they can be synthesized in polynomial time. Finally, we present experimental results on a variety of benchmarks and several natural examples that model various types of nested while loops in probabilistic programs and demonstrate that our approach is able to efficiently prove their almost-sure termination property.
Salience Models: A Computational Cognitive Neuroscience Review

The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model—so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely, its contribution to our theoretical, neural, and computational understanding of visual processing, as well as the spatial and temporal predictions for fixation distributions. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modelling, many of which tried to improve or add to the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks; however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modelling salience, starting from direct variations of the Itti and Koch salience model to sophisticated deep-learning architectures, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.
Improved Genetic Algorithm for High-Utility Itemset Mining

High-utility itemset mining (HUIM) is an important research topic in the data mining field. Typically, traditional HUIM algorithms must handle the exponential problem of huge search space when the database size or number of distinct items is very large. As an alternative and effective approach, evolutionary computation (EC)-based algorithms have been proposed to solve HUIM problems because they can obtain a set of nearly optimal solutions in limited time. However, it is still time-consuming for EC-based algorithms to find complete high-utility itemsets (HUIs) in transactional databases. To address this problem, we propose an HUIM algorithm based on an improved genetic algorithm (HUIM-IGA). In addition, a neighborhood exploration strategy is proposed to improve search efficiency for HUIs. To reduce missing HUIs, a population diversity maintenance strategy is employed in the proposed HUIM-IGA. An individual repair method is also introduced to reduce invalid combinations for discovering HUIs. In addition, an elite strategy is employed to prevent the loss of HUIs. Experimental results obtained on a set of real-world datasets demonstrate that the proposed algorithm can find complete HUIs in terms of the given minimum utility threshold, and the time-consuming of HUIM-IGA is relatively lower when mining the same number of HUIs than state-of-the-art EC-based HUIM algorithms.
Open-Ended Video Question Answering via Multi-Modal Conditional Adversarial Networks

As a challenging task in visual information retrieval, open-ended long-form video question answering automatically generates the natural language answer from the referenced video content according to the given question. However, the existing video question answering works mainly focus on the short-form video, which may be ineffectively applied for long-form video question answering directly, due to the insufficiency of modeling the semantic representation of long-form video content. In this paper, we study the problem of open-ended long-form video question answering from the viewpoint of hierarchical multi-modal conditional adversarial network learning. We propose the hierarchical attentional encoder network to learn the joint representation of long-form video content and given question with adaptive video segmentation. We then devise the reinforced decoder network to generate the natural language answer for open-ended video question answering with multi-modal conditional adversarial network learning. We construct three large-scale open-ended video question answering datasets. The extensive experiments validate the effectiveness of our method.
Room-temperature photonic logical qubits via second-order nonlinearities

Abstract Recent progress in nonlinear optical materials and microresonators has brought quantum computing with bulk optical nonlinearities into the realm of possibility. This platform is of great interest, not only because photonics is an obvious choice for quantum networks, but also as a promising route to quantum information processing at room temperature. We propose an approach for reprogrammable room-temperature photonic quantum logic that significantly simplifies the realization of various quantum circuits, and in particular, of error correction. The key element is the programmable photonic multi-mode resonator that implements reprogrammable bosonic quantum logic gates, while using only the bulk χ (2) nonlinear susceptibility. We theoretically demonstrate that just two of these elements suffice for a complete, compact error-correction circuit on a bosonic code, without the need for measurement or feed-forward control. Encoding and logical operations on the code are also easily achieved with these reprogrammable quantum photonic processors. An extrapolation of current progress in nonlinear optical materials and photonic circuits indicates that such circuitry should be achievable within the next decade.