Reviewerly/exHarmony huggingface dataset:
Derivative-Free Method For Composite Optimization With Applications To Decentralized Distributed Optimization

In this paper, we propose a new method based on the Sliding Algorithm from Lan (2016, 2019) for the convex composite optimization problem that includes two terms: smooth one and non-smooth one. Our method uses the stochastic noised zeroth-order oracle for the non-smooth part and the first-order oracle for the smooth part and it is the first method in the literature that uses such a mixed oracle for the composite optimization. We prove the convergence rate for the new method that matches the corresponding rate for the first-order method up to a factor proportional to the dimension of the space or, in some cases, its squared logarithm. We apply this method for the decentralized distributed optimization and derive upper bounds for the number of communication rounds for this method that matches known lower bounds. Moreover, our bound for the number of zeroth-order oracle calls per node matches the similar state-of-the-art bound for the first-order decentralized distributed optimization up to to the factor proportional to the dimension of the space or, in some cases, its squared logarithm.
Need Analysis for Developing Writing Skill Materials Using Facebook for English Undergraduate Students

This research is aiming at giving a contribution to the lecturersâ€™ knowledge on the design materials in using social media Facebook in teaching writing process to English Department students. Before conducted the developing module for Facebook-based writing instructional course, we surveyed and explored the studentâ€™s need. The stages of this research consisted of need analysis and document analysis. We conducted needs analysis to 141 English students and 2 lecturers of writing. Data were collected using need analysis questionnaire and interview. The analyzed documents in this research were lesson plan, syllabus and the existing textbooks. The data from questionnaire and interview were analyzed in quantitative and qualitative methods. The results concluded that: first, needs analysis is the basic of developing module for Facebook-based writing instructional course, in order that the material can be related to the studentsâ€™ needs, levels and lecturersâ€™ perception. The result of students need found urgent to develop module for facebook-based writing instructional course through the following criteria of interactive, self-contained, user friendly, online supporting, online social media, authentic, environmentally friendly, formal and informal environments, online evaluation, presented in visualÂ aids, supportÂ and facilitate the studentsâ€™Â academic and non-academic writing activities, topics of the materials should be interesting which provides a cultural background of students, the materials should be implemented by applying vocabulary, reading texts, grammar and basic skills in writing process, implemented in beginner level (Writing 1), used in class and out- class and the materials globally/ international context. Second, the lecturersâ€™ desires in teaching writing are to improve the studentsâ€™ skill to comprehend the materials. Third, the existing materials are unsuitable for the students, they prefer learning facilitated by electronic social media, Facebook, so they will more motivate in write.
Muhasebe Bilgi Sisteminde Kullanılan Yeni Bilgi Teknolojileri ve Bu Teknolojilerin Rolü

Up-to-date information technologies should be used in the Accounting Information System (MBS) in order to ensure a competitive environment between businesses and to respond quickly to opportunities.The effects of innovations brought by information technology have been reflected in the accounting information system.Thanks to the advantages of these technologies over the accounting information system, it is possible to monitor business and accounting processes synchronously.Especially, with access to the global internet network, simultaneous data sharing has increased.With the virtualization of data in the accounting information system, applications such as e-ledger, virtual storage, real-time reporting, and e-auditing have emerged.In the study, the role and effect of cloud accounting, blockchain, and big data technologies on the accounting information system in terms of data storage, data analytics, and reporting were examined.In addition, the advantages of these technologies to the accounting information system are included in the study.Innovative MBSs used in the processing, virtualization, storage, and reporting of financial and non-financial data of enterprises contribute to competitive advantage, efficiency, transparency, institutionalism, and intellectuality in enterprises.
Cycling@CHI: Towards a Research Agenda for HCI in the Bike Lane

In this workshop, we will explore and discuss future developments in mobile user-interfaces for cyclists and users of similar interfaces or services. We highlight the challenge of balancing safety and ecological validity in experiments, and how novel and improved evaluation methods can improve the current situation. We aim to bring together researchers with a strong background in designing and evaluating novel user interfaces in the domain of bicycles and mobility, as well as practitioners who build consumer products in that domain. The workshop’s goal is to explore novel ways of designing and evaluating user interfaces for cyclists and similar users when it comes to interacting with mobile devices and services on the ride.
QUERYD: A Video Dataset with High-Quality Text and Audio Narrations

We introduce QuerYD, a new large-scale dataset for retrieval and event localisation in video. A unique feature of our dataset is the availability of two audio tracks for each video: the original audio, and a high-quality spoken description of the visual content. The dataset is based on YouDescribe [1], a volunteer project that assists visually-impaired people by attaching voiced narrations to existing YouTube videos. This ever-growing collection of videos contains highly detailed, temporally aligned audio and text annotations. The content descriptions are more relevant than dialogue, and more detailed than previous description attempts, which can be observed to contain many superficial or uninformative descriptions. To demonstrate the utility of the QuerYD dataset, we show that it can be used to train and benchmark strong models for retrieval and event localisation. Data, code and models are made publicly available, and we hope that QuerYD inspires further research on video understanding with written and spoken natural language.
A Systematic Review of Augmented Reality Applications in Language Learning

Augmented reality (AR) is one of the emerging technologies gradually venturing into the education field. Although AR is strongly linked to subjects related to Science, Mathematics, and Technology in schools and tertiary education, there is no mention of AR in non-technical subjects, such as language. Thus, this study aims to discover the AR application trends in language learning and the language skills prevalent in AR usage. In this systematic literature review, AR-related research in language learning began in 2016 and has continued to be on the rise. Furthermore, the preferred language skills used with AR technology required lower cognitive levels, such as identifying words, understanding meanings, and spelling, and pronouncing words. Based on the gap indicating the rare usage of AR for more complex and critical language skills, such as reading and writing, this study hopes to enlighten the researchers, educators, and application developers to focus on developing AR applications for languages other than English, incorporate higher-order learning outcomes in the language learning activities, and pursue qualitative investigations.
Detection of effective genes in colon cancer: A machine learning approach

Nowadays, a variety of cancers have become common among humans which unfortunately are the cause of death for many of these people. Early detection and diagnosis of cancers can have a significant impact on the survival of patients and treatment cost reduction. Colon cancer is the third and the second main cause of women's and men's death worldwide among cancers. Hence, many researchers have been trying to provide new methods for early diagnosis of colon cancer. In this study, we apply statistical hypothesis tests such as t-test and Mann–Whitney–Wilcoxon and machine learning methods such as Neural Network, KNN and Decision Tree to detect the most effective genes in the vital status of colon cancer patients. We normalize the dataset using a new two-step method. In the first step, the genes within each sample (patient) are normalized to have zero mean and unit variance. In the second step, normalization is done for each gene across the whole dataset. Analyzing the results shows that this normalization method is more efficient than the others and improves the overall performance of the research. Afterwards, we apply unsupervised learning methods to find the meaningful structures in colon cancer gene expressions. In this regard, the dimensionality of the dataset is reduced by employing Principle Component Analysis (PCA). Next, we cluster the patients according to the PCA extracted features. We then check the labeling results of unsupervised learning methods using different supervised learning algorithms. Finally, we determine genes which have major impact on colon cancer mortality rate in each cluster. Our conducted study is the first which suggests that the colon cancer patients can be categorized into two clusters. In each cluster, 20 effective genes were extracted which can be important for early diagnosis of colon cancer. Many of these genes have been identified for the first time.
Adversarial Caching Training: Unsupervised Inductive Network Representation Learning on Large-Scale Graphs

Network representation learning (NRL) has far-reaching effects on data mining research, showing its importance in many real-world applications. NRL, also known as network embedding, aims at preserving graph structures in a low-dimensional space. These learned representations can be used for subsequent machine learning tasks, such as vertex classification, link prediction, and data visualization. Recently, graph convolutional network (GCN)-based models, e.g., GraphSAGE, have drawn a lot of attention for their success in inductive NRL. When conducting unsupervised learning on large-scale graphs, some of these models employ negative sampling (NS) for optimization, which encourages a target vertex to be close to its neighbors while being far from its negative samples. However, NS draws negative vertices through a random pattern or based on the degrees of vertices. Thus, the generated samples could be either highly relevant or completely unrelated to the target vertex. Moreover, as the training goes, the gradient of NS objective calculated with the inner product of the unrelated negative samples and the target vertex may become zero, which will lead to learning inferior representations. To address these problems, we propose an adversarial training method tailored for unsupervised inductive NRL on large networks. For efficiently keeping track of high-quality negative samples, we design a caching scheme with sampling and updating strategies that has a wide exploration of vertex proximity while considering training costs. Besides, the proposed method is adaptive to various existing GCN-based models without significantly complicating their optimization process. Extensive experiments show that our proposed method can achieve better performance compared with the state-of-the-art models.
Smoothing and Shrinking the Sparse Seq2Seq Search Space

Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences.While this setup has led to strong results in a variety of tasks, one unsatisfying aspect is its length bias: models give high scores to short, inadequate hypotheses and often make the empty string the argmax-the so-called cat got your tongue problem.Recently proposed entmax-based sparse sequence-to-sequence models present a possible solution, since they can shrink the search space by assigning zero probability to bad hypotheses, but their ability to handle word-level tasks with transformers has never been tested.In this work, we show that entmax-based models effectively solve the cat got your tongue problem, removing a major source of model error for neural machine translation.In addition, we generalize label smoothing, a critical regularization technique, to the broader family of Fenchel-Young losses, which includes both cross-entropy and the entmax losses.Our resulting label-smoothed entmax loss models set a new state of the art on multilingual grapheme-to-phoneme conversion and deliver improvements and better calibration properties on cross-lingual morphological inflection and machine translation for 7 language pairs.
Towards a Unifying Framework for Formal Theories of Novelty

Managing inputs that are novel, unknown, or out-of-distribution is critical as an agent moves from the lab to the open world. Novelty-related problems include being tolerant to novel perturbations of the normal input, detecting when the input includes novel items, and adapting to novel inputs. While significant research has been undertaken in these areas, a noticeable gap exists in the lack of a formalized definition of novelty that transcends problem domains. As a team of researchers spanning multiple research groups and different domains, we have seen, first hand, the difficulties that arise from ill-specified novelty problems, as well as inconsistent definitions and terminology. Therefore, we present the first unified framework for formal theories of novelty and use the framework to formally define a family of novelty types. Our framework can be applied across a wide range of domains, from symbolic AI to reinforcement learning, and beyond to open world image recognition. Thus, it can be used to help kick-start new research efforts and accelerate ongoing work on these important novelty-related problems.
Does relativistic motion always degrade quantum Fisher information?

We investigate the ultimate estimation precision, characterized by the quantum Fisher information, of a two-level atom as a detector which is coupled to massless scalar field in the Minkowski vacuum. It has been shown that for an inertial detector moving with a constant velocity, its quantum Fisher information is completely unaffected by the velocity, however, it still decays over time due to the decoherence caused by the interaction between the atom and the field. In addition, for a uniformly accelerated detector ($w=0$) moving along spatially straight line, the accelerated motion will reduce the quantum Fisher information in the estimation of state parameters. However, when the detector trajectory is generated by a combination of the linear accelerated motion and a component of the four-velocity $w=dy/d\tau$, we find quite unlike the previous results that, for the non-relativistic case $(w\ll1)$, the acceleration could degrade the quantum Fisher information, while the four-velocity component will suppress the degradation of the quantum Fisher information, and thus could enhance the precision of parameters estimation. Furthermore, in the case for ultra-relativistic velocities $(w\rightarrow\infty)$, although the detector still interacts with the environment, it behaves as if it were a closed system as a consequence of relativity correction associated to the velocity, and the quantum Fisher information in this case can be shield from the effect of the external environment, and thus from the relativistic motion.
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks

Jinghui Qin, Xiaodan Liang, Yining Hong, Jianheng Tang, Liang Lin. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation

Panoptic segmentation (PS) is a complex scene understanding task that requires providing high-quality segmentation for both thing objects and stuff regions. Previous methods handle these two classes with semantic and instance segmentation modules separately, following with heuristic fusion or additional modules to resolve the conflicts between the two outputs. This work simplifies this pipeline of PS by consistently modeling the two classes with a novel PS framework, which extends a detection model with an extra module to predict category- and instance-aware pixel embedding (CIAE). CIAE is a novel pixel-wise embedding feature that encodes both semantic-classification and instance-distinction information. At the inference process, PS results are simply derived by assigning each pixel to a detected instance or a stuff class according to the learned embedding. Our method not only demonstrates fast inference speed but also the first one-stage method to achieve comparable performance to two-stage methods on the challenging COCO benchmark.
Face-Computer Interface (FCI): Intent Recognition Based on Facial Electromyography (fEMG) and Online Human-Computer Interface With Audiovisual Feedback

Patients who have lost limb control ability, such as upper limb amputation and high paraplegia, are usually unable to take care of themselves. Establishing a natural, stable, and comfortable human-computer interface (HCI) for controlling rehabilitation assistance robots and other controllable equipments will solve a lot of their troubles. In this study, a complete limbs-free face-computer interface (FCI) framework based on facial electromyography (fEMG) including offline analysis and online control of mechanical equipments was proposed. Six facial movements related to eyebrows, eyes, and mouth were used in this FCI. In the offline stage, 12 models, eight types of features, and three different feature combination methods for model inputing were studied and compared in detail. In the online stage, four well-designed sessions were introduced to control a robotic arm to complete drinking water task in three ways (by touch screen, by fEMG with and without audio feedback) for verification and performance comparison of proposed FCI framework. Three features and one model with an average offline recognition accuracy of 95.3%, a maximum of 98.8%, and a minimum of 91.4% were selected for use in online scenarios. In contrast, the way with audio feedback performed better than that without audio feedback. All subjects completed the drinking task in a few minutes with FCI. The average and smallest time difference between touch screen and fEMG under audio feedback were only 1.24 and 0.37 min, respectively.
Subdimensional Expansion for Multi-Objective Multi-Agent Path Finding

Conventional multi-agent path planners typically determine a path that optimizes a single objective, such as path length. Many applications, however, may require multiple objectives, say time-to-completion and fuel use, to be simultaneously optimized in the planning process. Often, these criteria may not be directly compared and sometimes lie in competition with each other. Simply applying standard multi-objective search algorithms to multi-agent path finding may prove to be inefficient because the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents. This letter presents an approach that bypasses this so-called curse of dimensionality by leveraging our prior multi-agent work with a framework called subdimensional expansion. One example of subdimensional expansion, when applied to A$^*$, is called M$^*$ and M$^*$ was limited to a single objective function. We combine principles of dominance and subdimensional expansion to create a new algorithm, named multi-objective M$^*$ (MOM$^*$), which dynamically couples agents for planning only when those agents have to “interact” with each other. MOM$^*$ computes the complete Pareto-optimal set for multiple agents efficiently and naturally trades off sub-optimal approximations of the Pareto-optimal set and computational efficiency. Our approach is able to find the complete Pareto-optimal set for problem instances with hundreds of solutions which the standard multi-objective A$^*$ algorithms could not find within a bounded time.
Modular, compositional, and executable formal semantics for LLVM IR

This paper presents a novel formal semantics, mechanized in Coq, for a large, sequential subset of the LLVM IR. In contrast to previous approaches, which use relationally-specified operational semantics, this new semantics is based on monadic interpretation of interaction trees, a structure that provides a more compositional approach to defining language semantics while retaining the ability to extract an executable interpreter. Our semantics handles many of the LLVM IR's non-trivial language features and is constructed modularly in terms of event handlers, including those that deal with nondeterminism in the specification. We show how this semantics admits compositional reasoning principles derived from the interaction trees equational theory of weak bisimulation, which we extend here to better deal with nondeterminism, and we use them to prove that the extracted reference interpreter faithfully refines the semantic model. We validate the correctness of the semantics by evaluating it on unit tests and LLVM IR programs generated by HELIX.
Pedestrian- and Vehicle-Detection Algorithm Based on Improved Aggregated Channel Features

In advanced driver-assistance systems (ADAS), the accuracy and real-time performance of pedestrian- and vehicle-detection algorithms based on vision sensors are crucial for safety. Here, a lightweight detection algorithm based on aggregated channel features (ACFs),consisting of a context pixel ACF (CP-ACF) pedestrian detector and a multiview ACF (Mv-ACF) vehicle detector, is proposed to rapidly and precisely understand road scenes. The former fuses local and context information to improve the robustness to pedestrian deformation, while the latter contains a number of subclass detectors to alleviate intraclass differences due to different viewing angles. Compared to the original ACF, the CP-ACF pedestrian detector reduces the average miss rate (AMR) by 6.34%. The Mv-ACF vehicle detector improves the average precision (AP) by 40.26% on average at easy, moderate and hard levels. This remarkable effectiveness is due to the spectrum clustering of multiview samples and the resulting integration of these subclass detectors via confidence score calibration, which reduces the intraclass differences of vehicles. Since feature extraction takes up 68.8% of the total detection time, a mechanism of feature sharing between pedestrian and vehicle detectors is advanced to reduce the time spent in feature extraction. A strategy based on ground-plane constraints (GPCs) is proposed to control false detection of pedestrians and vehicles by incorporating road prior information, which reduces the AMR by 1.07% for CP-ACF pedestrian detectors and improves the AP by 0.27% on average for Mv-ACF vehicle detectors. Thus, the proposed algorithm can effectively control false detection by road prior information.