Reviewerly/exHarmony huggingface dataset:
Interactive Learning with Mutual Explanations in Relational Domains

With the growing number of applications of machine learning in complex real-world domains machine learning research has to meet new requirements to deal with the imperfections of real world data and the legal as well as ethical obligations to make classifier decisions transparent and comprehensible. In this contribution, arguments for interpretable and interactive approaches to machine learning are presented. It is argued that visual explanations are often not expressive enough to grasp critical information which relies on relations between different aspects or sub-concepts. Consequently, inductive logic programming (ILP) and the generation of verbal explanations from Prolog rules is advocated. Interactive learning in the context of ILP is illustrated with the Dare2Del system which helps users to manage their digital clutter. It is shown that verbal explanations overcome the explanatory one-way street from AI system to user. Interactive learning with mutual explanations allows the learning system to take into account not only class corrections but also corrections of explanations to guide learning. We propose mutual explanations as a building-block for human-like computing and an important ingredient for human AI partnership.
GEOMETRIC SPATIAL ABILITY AS AN ELEMENT OF COGNITIVE LEARNING PROCESS

University professors more and more often face serious problems of learning geometry and graphic disciplines. In this regard, it is very relevant to obtain a maximum efficient method to teach geometry and graphic disciplines. The purpose of the study is to test the hypothesis that the cause of problems arising in the process of mastering the geometric and graphic disciplines is the violation of continuity of the educational material and the low level of spatial ability of students. In the research, interviewing method (testing) and statistic method have been applied to process the results. It is established that spatial imagination helps a person to perceive the shape of geometric objects by involving him/her into the cognitive process of feeling and control of spatial objects. Students who learned in school technical drawing the same as students who did not learn experience difficulties in mastering the geometric and graphic disciplines. For students who learned technical drawing in schools, the cause of bad retention of the material of the geometric and graphic discipline is low geometric spatial ability. For students who did not learn technical drawing in school, but who demonstrated a high geometric spatial ability, the cause of difficulties in the retention of the geometric and graphic disciplines are connected with the violation of continuity of the educational material. To effectively teach the geometric and graphic disciplines, it is necessary to apply methods of developments of the geometric spatial ability and to form the content of discipline using the principle of continuity.
Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems.
Towards Automatic Speech to Sign Language Generation

We aim to solve the highly challenging task of generating continuous sign language videos solely from speech segments for the first time. Recent efforts in this space have focused on generating such videos from human-annotated text transcripts without considering other modalities. However, replacing speech with sign language proves to be a practical solution while communicating with people suffering from hearing loss. Therefore, we eliminate the need of using text as input and design techniques that work for more natural, continuous, freely uttered speech covering an extensive vocabulary. Since the current datasets are inadequate for generating sign language directly from speech, we collect and release the first Indian sign language dataset comprising speech-level annotations, text transcripts, and the corresponding sign-language videos. Next, we propose a multi-tasking transformer network trained to generate signer's poses from speech segments. With speech-to-text as an auxiliary task and an additional cross-modal discriminator, our model learns to generate continuous sign pose sequences in an end-to-end manner. Extensive experiments and comparisons with other baselines demonstrate the effectiveness of our approach. We also conduct additional ablation studies to analyze the effect of different modules of our network. A demo video containing several results is attached to the supplementary material.
NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces

External control of agent-based models is vital for complex adaptive systems research. Often these experiments require vast numbers of simulation runs and are computationally expensive. NetLogo is the language of choice for most agent-based modelers but lacks direct API access through Python. NL4Py is a Python package for the parallel execution of NetLogo simulations via Python, designed for speed, scalability, and simplicity of use. NL4Py provides access to the large number of open-source machine learning and analytics libraries of Python and enables convenient and efficient parallelization of NetLogo simulations with minimal coding expertise by domain scientists.
Schopenhauer’s Partition Diagrams and Logical Geometry

The paper examines Schopenhauer’s complex diagrams from the Berlin Lectures of the 1820 s, which show certain partitions of classes. Drawing upon ideas and techniques from logical geometry, we show that Schopenhauer’s partition diagrams systematically give rise to a special type of Aristotelian diagrams, viz. (strong) \(\alpha \)-structures.
Distributed Stochastic Optimization with Large Delays

The recent surge of breakthroughs in machine learning and artificial intelligence has sparked renewed interest in large-scale stochastic optimization problems that are universally considered hard. One of the most widely used methods for solving such problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms that result from parallelizing stochastic gradient descent on distributed computing architectures (possibly) asychronously. However, a key obstacle in the efficient implementation of DASGD is the issue of delays: when a computing node contributes a gradient update, the global model parameter may have already been updated by other nodes several times over, thereby rendering this gradient information stale. These delays can quickly add up if the computational throughput of a node is saturated, so the convergence of DASGD may be compromised in the presence of large delays. Our first contribution is that, by carefully tuning the algorithm’s step size, convergence to the critical set is still achieved in mean square, even if the delays grow unbounded at a polynomial rate. We also establish finer results in a broad class of structured optimization problems (called variationally coherent), where we show that DASGD converges to a global optimum with a probability of one under the same delay assumptions. Together, these results contribute to the broad landscape of large-scale nonconvex stochastic optimization by offering state-of-the-art theoretical guarantees and providing insights for algorithm design.
Effects of display lag on vection and presence in the Oculus Rift HMD

Head-mounted display (HMD)-based virtual reality (VR) is ideally suited for presence and generating compelling visual experiences of self-motion, but users can suffer from side effects associated with head-to-display lag. We used the Oculus Rift HMD (consumer release – CV1) to simulate forward self-motion in depth. Observers made continuous yaw head movements at approximately 0.5 Hz or 1.0 Hz while viewing these self-motion simulations. We examined the perceptual effects of increasing the display lag, by adding lag to the baseline lag of the system (estimated to be approximately 5.3 ms or 0.5 frames per second). We found that increasing the head-to-display lag up to 212 ms reduced the presence and the strength of vection. In addition, faster (1.0 Hz) head oscillations were found to generate weaker presence and vection in the virtual environment than the slower (0.5 Hz) head oscillations. We also found that a positive correlation between vection and presence (found previously) persists across a wide range of head-to-display lags, and, increasing lag from a very low baseline level still impaired both experiences. Both vection and presence in virtual environments can therefore be impaired by either increasing head-display lag or making more rapid angular head movements.
Blockchain Technology, Inter-Organizational Relationships, and Management Accounting: A Synthesis and a Research Agenda

SYNOPSIS Blockchain technology is increasingly emerging as an important organizational phenomenon, especially for collaboration across firm boundaries. Over the past three decades, accounting scholars have shown significant interest in management accounting and control mechanisms that are used by actors to sustain inter-organizational relationships. We outline fundamental technical features and limitations of permissioned blockchain technology and analytically propose blockchain as an empirical concept with implications for management accounting practices that underpin inter-organizational collaboration, trust, control, and information exchange. Particular focus of the analysis is on the interplay between the technical capabilities of blockchain technology and inter-organizational management control procedures. Based on this analysis, we develop a series of propositions that theorize how these procedures affect the way in which blockchain is enacted in IORs, and how they are affected by blockchain in turn. The paper concludes with a research agenda for accounting scholars and offers directions for further research.
P-k-means: k-means Using Partition Based Cluster Initialization Method

The k-Means algorithm is extensively used in a number of data clustering applications. In basic k-means, initial cluster centroids are selected on random basis. As a result, every run of k-means leads to the formation of different clusters. Hence, accuracy and performance of k-means is susceptible to the selection of initial cluster centroids. Therefore, careful initialization of cluster centroids plays a major role on accuracy and performance of the k-means algorithm. In view of this, a new k-means using Partition based Cluster Initialization method called as ‘P-k-means’ is proposed in this paper. The experiment is carried out on six different datasets. The empirical results are compared using various external and internal clustering validation measures. The comparative results show that P-k-means is better than basic k-means.
Asking questions on handwritten document collections

This work addresses the problem of Question Answering (QA) on handwritten document collections. Unlike typical QA and Visual Question Answering (VQA) formulations where the answer is a short text, we aim to locate a document snippet where the answer lies. The proposed approach works without recognizing the text in the documents. We argue that the recognition-free approach is suitable for handwritten documents and historical collections where robust text recognition is often difficult. At the same time, for human users, document image snippets containing answers act as a valid alternative to textual answers. The proposed approach uses an off-the-shelf deep embedding network which can project both textual words and word images into a common sub-space. This embedding bridges the textual and visual domains and helps us retrieve document snippets that potentially answer a question. We evaluate results of the proposed approach on two new datasets: (i) HW-SQuAD: a synthetic, handwritten document image counterpart of SQuAD1.0 dataset and (ii) BenthamQA: a smaller set of QA pairs defined on documents from the popular Bentham manuscripts collection. We also present a thorough analysis of the proposed recognition-free approach compared to a recognition-based approach which uses text recognized from the images using an OCR. Datasets presented in this work are available to download at docvqa.org
Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

Abstract The discriminative correlation filter (DCF)-based tracking methods have achieved remarkable performance in visual tracking. However, the existing DCF paradigm still suffers from dilemmas such as boundary effect, filter degradation, and aberrance. To address these problems, we propose a spatio-temporal joint aberrance suppressed regularization (STAR) correlation filter tracker under a unified framework of response map. Specifically, a dynamic spatio-temporal regularizer is introduced into the DCF to alleviate the boundary effect and filter degradation, simultaneously. Meanwhile, an aberrance suppressed regularizer is exploited to reduce the interference of background clutter. The proposed STAR model is effectively optimized using the alternating direction method of multipliers (ADMM). Finally, comprehensive experiments on TC128, OTB2013, OTB2015 and UAV123 benchmarks demonstrate that the STAR tracker achieves compelling performance compared with the state-of-the-art (SOTA) trackers.
Semi-supervised label distribution learning via projection graph embedding

Label distribution learning (LDL) is a new machine learning paradigm that addresses label ambiguity by emphasizing the relevance of each label to a particular instance. In supervised learning, many LDL algorithms have been proposed, which often require a large amount of well-annotated training data to achieve good performance. However, annotating a label distribution is more complicated and expensive than annotating a single label or multiple labels with logical values of 0 and 1. Thus, we propose a projection graph embedding algorithm for semi-supervised label distribution learning (PGE-SLDL). Specifically, we seek a potential space by orthogonal neighborhood preserving projections, named capture space. This capture space is used to select more valuable features and construct a graph that contains more accurate data structure information. We utilize the sample correlation information contained between graph nodes to recover the unknown label distribution. In addition, compared with fixed graphs in traditional semi-supervised learning, we carry out projection and graph construction simultaneously to obtain a self-updating projection graph, which is more helpful to learn label distribution. The experimental results validate the effectiveness of the proposed algorithm.
Generalizable synthesis through unification

The generalizability of PBE solvers is the key to the empirical synthesis performance. Despite the importance of generalizability, related studies on PBE solvers are still limited. In theory, few existing solvers provide theoretical guarantees on generalizability, and in practice, there is a lack of PBE solvers with satisfactory generalizability on important domains such as conditional linear integer arithmetic (CLIA). In this paper, we adopt a concept from the computational learning theory, Occam learning, and perform a comprehensive study on the framework of synthesis through unification (STUN), a state-of-the-art framework for synthesizing programs with nested if-then-else operators. We prove that Eusolver, a state-of-the-art STUN solver, does not satisfy the condition of Occam learning, and then we design a novel STUN solver, PolyGen, of which the generalizability is theoretically guaranteed by Occam learning. We evaluate PolyGen on the domains of CLIA and demonstrate that PolyGen significantly outperforms two state-of-the-art PBE solvers on CLIA, Eusolver and Euphony, on both generalizability and efficiency.
Brief communication: Effective earthquake early warning systems: appropriate messaging and public awareness roles

Abstract. The earthquake early warning systems (EEWSs) in China have achieved great progress, with warning alerts being successfully delivered to the public in some regions. We examined the performance of the EEWS in China's Sichuan Province during the 2019 Changning earthquake. Although its technical effectiveness was tested with the first alert released 10 s after the earthquake, we found that a big gap existed between the EEWS's message and the public's response. We highlight the importance of EEWS alert effectiveness and public participation for long-term resiliency, such as delivering useful alert messages through appropriate communication channels and training people to understand and properly respond.
CDCEO'21 - First Workshop on Complex Data Challenges in Earth Observation

High-resolution remote sensing technology for Earth Observation (EO) has radically changed how we monitor the state of our planet around the clock. An effective interpretation of the resulting complex large-scale time series adopts the best machine learning techniques from signal processing, computer vision, pattern recognition, and artificial intelligence. The First Workshop on Complex Data Challenges in Earth Observation was open to both method development and advanced applications in a wide range of related topics, including image and signal processing, gap-filling, data fusion, feature extraction, prediction of spatio-temporal features, and the detection of rules underlying the observed state transitions and causal relationships. The full agenda, featuring keynotes and a selection of high quality contributed talks is available online at www.iarai.ac.at/cdceo21
Algorithmic Balancing of Familiarity, Similarity, &amp; Discovery in Music Recommendations

Algorithmic recommendations shape music consumption at scale, and understanding the role different behavioral aspects play in how content is consumed, is a central question for music streaming platforms. Focusing on the notions of familiarity, similarity and discovery, we identify the need for explicit consideration and optimization of such objectives, and establish the need to efficiently balance them when generating algorithmic recommendations for users. We posit that while familiarity helps drive short term engagement, jointly optimizing for discovery enables the platform to influence and shape consumption across suppliers. We propose a multi-level ordered-weighted averaging based objective balancer to help maintain a healthy balance with respect to familiarity and discovery objectives, and conduct a series of offline evaluations and online AB tests, to demonstrate that despite the presence of strict trade-offs, we can achieve wins on both satisfaction and discover centric objectives. Our proposed methods and insights have implications for the design and deployment of practical approaches for music recommendations, and our findings demonstrate that they can lead to substantial improvements on recommendation quality on one of the world's largest music streaming platforms.
An object detection network based on YOLOv4 and improved spatial attention mechanism

In recent years, the research on object detection has been intensified. A large number of object detection results are applied to our daily life, which greatly facilitates our work and life. In this paper, we propose a more effective object detection neural network model ENHANCE_YOLOV4. We studied the effects of several attention mechanisms on YOLOV4, and finally concluded that spatial attention mechanism had the best effect on YOLOV4. Therefore, based on previous studies, this paper introduces Dilated Convolution and one-by-one convolution into the spatial attention mechanism to expand the receptive field and combine channel information. Compared with CBAM and BAM, which are composed of spatial attention and channel attention, this improved spatial attention module reduces model parameters and improves detection capabilities. We built a new network model by embedding improved spatial attention module in the appropriate place in YOLOV4. And this paper proves that the detection accuracy of this network structure on the VOC data set is increased by 0.8%, and the detection accuracy on the coco data set is increased by 7%when the calculation performance is increased a little.
Multi-objective Sparrow Search Optimization for Task Scheduling in Fog-Cloud-Blockchain Systems

In this paper, we first design a system that integrates a blockchain network into a fog-cloud environment to improve the transparency as well as avoid the attack from suspicious parties. We identify task scheduling as the most prominent and crucial issue in such a system and model it as a multi-objective optimization problem. To solve this problem, we propose a multi-objective version of the Sparrow Search Algorithm (SSA), called MO-SSA. Our experimental studies compare MO-SSA with other well-known optimization algorithms (NSGA-II, NSGA-III, and MO-ALO). Results show that MO-SSA archives better results than most of the baseline algorithms on several performance metrics.
Geo-BERT Pre-training Model for Query Rewriting in POI Search

Query Rewriting (QR) is proposed to solve the problem of the word mismatch between queries and documents in Web search.Existing approaches usually model QR with an end-to-end sequence-to-sequence (seq2seq) model.The state-of-the-art Transformer-based models can effectively learn textual semantics from user session logs, but they often ignore users' geographic location information that is crucial for the Point-of-Interest (POI) search of map services.In this paper, we proposed a pretraining model, called Geo-BERT, to integrate semantics and geographic information in the pre-trained representations of POIs.Firstly, we simulate POI distribution in the real world as a graph, in which nodes represent POIs and multiple geographic granularities.Then we use graph representation learning methods to get geographic representations.Finally, we train a BERT-like pre-training model with text and POIs' graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search.The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services.