Reviewerly/exHarmony huggingface dataset:
Improving the Generalizability of the Dense Passage Retriever Using Generated Datasets

Dense retrieval methods have surpassed traditional sparse retrieval methods for open-domain retrieval. While these methods, such as the Dense Passage Retriever (DPR), work well on datasets or domains they have been trained on, there is a noticeable loss in accuracy when tested on out-of-distribution and out-of-domain datasets. We hypothesize that this may be, in large part, due to the mismatch in the information available to the context encoder and the query encoder during training. Most training datasets commonly used for training dense retrieval models contain an overwhelming majority of passages where there is only one query from a passage. We hypothesize that this imbalance encourages dense retrieval models to overfit to a single potential query from a given passage leading to worse performance on out-of-distribution and out-of-domain queries. To test this hypothesis, we focus on a prominent dense retrieval method, the dense passage retriever, build generated datasets that have multiple queries for most passages, and compare dense passage retriever models trained on these datasets against models trained on single query per passage datasets. Using the generated datasets, we show that training on passages with multiple queries leads to models that generalize better to out-of-distribution and out-of-domain test datasets.
SR-CoMbEr: Heterogeneous Network Embedding Using Community Multi-view Enhanced Graph Convolutional Network for Automating Systematic Reviews

Systematic reviews (SRs) are a crucial component of evidence-based clinical practice. Unfortunately, SRs are labor-intensive and unscalable with the exponential growth in literature. Automating evidence synthesis using machine learning models has been proposed but solely focuses on the text and ignores additional features like citation information. Recent work demonstrated that citation embeddings can outperform the text itself, suggesting that better network representation may expedite SRs. Yet, how to utilize the rich information in heterogeneous information networks (HIN) for network embeddings is understudied. Existing HIN models fail to produce a high-quality embedding compared to simply running state-of-the-art homogeneous network models. To address existing HIN model limitations, we propose SR-CoMbEr, a community-based multi-view graph convolutional network for learning better embeddings for evidence synthesis. Our model automatically discovers article communities to learn robust embeddings that simultaneously encapsulate the rich semantics in HINs. We demonstrate the effectiveness of our model to automate 15 SRs.
Where a Little Change Makes a Big Difference: A Preliminary Exploration of Children’s Queries

This paper contributes to the discussion initiated in a recent SIGIR paper describing a gap in the information retrieval (IR) literature on query understanding–where they come from and whether they serve their purpose. Particularly the connection between query variability and search engines regarding consistent and equitable access to all users. We focus on a user group typically underserved: children. Using preliminary experiments (based on logs collected in the classroom context) and arguments grounded in children IR literature, we emphasize the importance of dedicating research efforts to interpreting queries formulated by children and the information needs they elicit. We also outline open problems and possible research directions to advance knowledge in this area, not just for children but also for other often-overlooked user groups and contexts.
SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval

Recent progress in pose-estimation methods enables the extraction of sufficiently-precise 3D human skeleton data from ordinary videos, which offers great opportunities for a wide range of applications. However, such spatio-temporal data are typically extracted in the form of a continuous skeleton sequence without any information about semantic segmentation or annotation. To make the extracted data reusable for further processing, there is a need to access them based on their content. In this paper, we introduce a universal retrieval approach that compares any two skeleton sequences based on temporal order and similarities of their underlying segments. The similarity of segments is determined by their content-preserving low-dimensional code representation that is learned using the Variational AutoEncoder principle in an unsupervised way. The quality of the proposed representation is validated in retrieval and classification scenarios; our proposal outperforms the state-of-the-art approaches in effectiveness and reaches speed-ups up to 64x on common skeleton sequence datasets.
GNN Transformation Framework for Improving Efficiency and Scalability

We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our framework are two-fold; 1) it transforms various non-scalable GNNs to scale well to large-scale graphs by separating local feature aggregation from weight learning in their graph convolution, 2) it efficiently executes precomputation on GPU for large-scale graphs by decomposing their edges into small disjoint and balanced sets. Through extensive experiments with large-scale graphs, we demonstrate that the transformed GNNs run faster in training time than existing GNNs while achieving competitive accuracy to the state-of-the-art GNNs. Consequently, our transformation framework provides simple and efficient baselines for future research on scalable GNNs.
Recommending Related Products Using Graph Neural Networks in Directed Graphs

Related product recommendation (RPR) is pivotal to the success of any e-commerce service. In this paper, we deal with the problem of recommending related products i.e., given a query product, we would like to suggest top-k products that have high likelihood to be bought together with it. Our problem implicitly assumes asymmetry i.e., for a phone, we would like to recommend a suitable phone case, but for a phone case, it may not be apt to recommend a phone because customers typically would purchase a phone case only while owning a phone. We also do not limit ourselves to complementary or substitute product recommendation. For example, for a specific night wear t-shirt, we can suggest similar t-shirts as well as track pants. So, the notion of relatedness is subjective to the query product and dependent on customer preferences. Further, various factors such as product price, availability lead to presence of selection bias in the historical purchase data, that needs to be controlled for while training related product recommendations model. These challenges are orthogonal to each other deeming our problem non-trivial. To address these, we propose DAEMON, a novel Graph Neural Network (GNN) based framework for related product recommendation, wherein the problem is formulated as a node recommendation task on a directed product graph. In order to capture product asymmetry, we employ an asymmetric loss function and learn dual embeddings for each product, by appropriately aggregating features from its neighborhood. DAEMON leverages multi-modal data sources such as catalog metadata, browse behavioral logs to mitigate selection bias and generate recommendations for cold-start products. Extensive offline experiments show that DAEMON outperforms state-of-the-art baselines by 30–160% in terms of HitRate and MRR for the node recommendation task. In the case of link prediction task, DAEMON presents 4–16% AUC gains over state-of-the-art baselines. DAEMON delivers significant improvement in revenue and sales as measured through an A/B experiment.
Data preparation in crowdsourcing for pedagogical purposes

One way to stimulate the use of corpora in language education is by making pedagogically appropriate corpora, labeled with different types of problems (sensitive content, offensive language, structural problems). However, manually labeling corpora is extremely time-consuming and a better approach should be found. We thus propose a combination of two approaches to the creation of problem-labeled pedagogical corpora of Dutch, Estonian, Slovene and Brazilian Portuguese: the use of games with a purpose and of crowdsourcing for the task. We conducted initial experiments to establish the suitability of the crowdsourcing task, and used the lessons learned to design the Crowdsourcing for Language Learning (CrowLL) game in which players identify problematic sentences, classify them, and indicate problematic excerpts. The focus of this paper is on data preparation, given the crucial role that such a stage plays in any crowdsourcing project dealing with the creation of language learning resources. We present the methodology for data preparation, offering a detailed presentation of source corpora selection, pedagogically oriented GDEX configurations, and the creation of lemma lists, with a special focus on common and language-dependent decisions. Finally, we offer a discussion of the challenges that emerged and the solutions that have been implemented so far.
Twitter as accidental authority

This chapter explores Twitter’s moderation of authoritative sources and their audience’s claims concerning COVID-19 treatments, transmission and prevention techniques. It examines how they diverge over time, and how Twitter intervenes in resulting debates via content moderation guidelines and techniques. It argues that as public health organizations and heads of state struggle to maintain consensus among themselves and with their Twitter audiences on these issues Twitter exceptionally steps in as an authority in its own right. It does so by flagging, suspending and deleting contents, including those of authoritative sources that threaten to disrupt a common understanding of the virus and vital health information.
Object tracking in surveillance video based on Siamese network considering geographical spatiotemporal constraints

Abstract Identifying and tracking objects in surveillance videos is an important means of mining information during surveillance. Currently, most object‐tracking methods rely only on image features, which cannot accurately express the motion of the tracked object in real geographical scenes and are easily influenced by occlusion and surrounding objects having similar features. However, tracked objects, such as pedestrians and vehicles, usually move in geographical space with fixed patterns of motion, and the motion in a short time is constrained by geographical space and time, the motion trajectory is predictable, and the range of motion is limited. Therefore, based on the SiamFC object tracking framework, this study introduces geographical spatiotemporal constraints into the tracking framework and proposes the GeoSiamFC method. The objective of this is to: (1) construct the mapping relationship between geographical space and image space to solve the problem that the pixel movement within the image after perspective imaging cannot accurately express the motion of the tracked object in a real geographical scene; (2) add candidate search areas according to the predicted trajectory location to correct the tracking errors caused by the occlusion of the object; and (3) focus on the search for the range of motion of the mapped pixel within the image space according to the limited geographical range of motion of the tracked objects in a certain time to reduce the interference of similar objects within the search area. In this study, separate experiments were conducted on a common test dataset using multiple methods to deal with challenges such as occlusion and illumination changes. In addition, a robust test dataset with noise addition and luminance adjustment based on the common test dataset was used. The results show that GeoSiamFC outperforms other object‐tracking methods in the common test dataset with a precision score of 0.995 and a success score of 0.756 compared with most other object‐tracking algorithms under the base condition of using only shallow networks. Moreover, GeoSiamFC maintained the highest precision score (0.970) and high success score (0.734) in the more challenging robust test dataset as well. The tracking speed of 59 frames per second far exceeds the real‐time requirement of 25 FPS. Geographical spatiotemporal constraints were considered to improve tracker performance while providing real‐time feedback on the motion trajectory of the target in geographical space. Thus, the proposed method is suitable for real‐time tracking of the motion trajectory of a target in real geographical scenes in various surveillance videos.
Simulation Research on Path Planning of Manipulator Based on RRT* Improved Algorithm

Path planning refers to searching for a collision-free path between the starting point and the target point according to some evaluation criteria in the environment with obstacles, which has always been the research focus in the field of robotics. In recent years, RRT* algorithm has attracted much attention because it can realize the gradual optimal solution of path planning, but it still has some shortcomings such as slow convergence speed, low path utilization rate and unsmooth path. In addition, unlike the path planning of mobile robots, which only needs to consider the path collision, the mechanical arm belongs to a series robot, and the links are clamped to each other, which directly affects the pose of the end effector through the change of the joint angle, so it is relatively more complicated to consider the rod collision. The main work of this paper is to propose an improved algorithm I-RRT* based on RRT *, and apply it to the path planning of manipulator. The simulation results show that this algorithm can well complete the task of path planning of manipulator, and it is advanced compared with traditional algorithms.
Bit duplication technique to generate hard QUBO problems

Quadratic Unconstrained Binary Optimization (QUBO) is a combinatorial optimization to find an optimal binary solution vector that minimizes the energy value defined by a quadratic formula of binary variables in the vector. The main contribution of this paper is to present the bit duplication technique, which can generate a hard QUBO problem from any QUBO problem so that their optimal solutions take the same energy value. The idea of the bit duplication technique is to duplicate bits and then to give constraints so that the corresponding two bits take the same binary values. By this technique, any QUBO problem with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> bits is converted to a hard QUBO problem with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2n$</tex> bits. We use random QUBO problems and N-Queen QUBO problems for experiments. The performance of QUBO solvers including Gurobi optimizer, Fixstars Amplify AE, OpenJij with SA, D-Wave Hybrid solver, and ABS QUBO solver are evaluated for solving these QUBO problems. The experimental results show that generated QUBO problems by the bit duplication technique are much harder than the original QUBO problems. Hence, the bit duplication technique is a potent method to generate hard QUBO problems and generated QUBO problems can be used as benchmark problems for evaluating the search performance of QUBO solvers.
Factors That Affect the Effectiveness of Management Accounting Software

This study aims to examine the suitability of the conceptual model regarding the effect of business strategy, user competence, organizational structure on the Effectiveness of Management Accounting Software (MAS), and how the influence between the variables studied during the Covid 19 Pandemic took place. Quantitative methods are used to test the suitability of the proposed model and to determine the predicted effect between the variables studied. The data were tested using Covarian Base Structural Equation Model (CB-SEM) with Lisrell 8.5 software. This study uses primary data collected through questionnaires to a population of 118 State-Owned Enterprises (SOEs) Management Accounting departments in Indonesia, with a selected sample size of 100 SOEs, which were selected using a simple random technique. The results of this study succeeded in confirming the conceptual model developed by the researcher and empirically proving the influence of business strategy on MAS Effectiveness, user competence on MAS Effectiveness and organizational structure on MAS Effectiveness in Indonesian SOEs companies. The effectiveness of MAS depends on the relevance of the needs of its users. The right business strategy, effectively provides relevant information for the company to design MAIS according to user needs, so that the company's operational activities run effectively and efficiently, the company is able to implement a Cost Reduction Strategy through production cost savings, production process accuracy, implementing product differentiation and low pricing strategy with a focus on customer needs during the Covid-19 pandemic. The results of this study contribute to producing strategic management accounting information to anticipate business continuity during and after the Covid 19 Pandemic and to help overcome the crisis due to the Covid 19 pandemic in the early stages of the Covid 19 Pandemic by optimizing business strategy, organizational structure, and company HR competencies.
A Novel Classification Method for Group Decision-Making Dimensions

This paper presents a novel and comprehensive classification mechanism that groups numerous dimensions associated with group decision-making approaches. We identify three broad categorizations of group decision-making literature. The classified dimensions are clustered by whether they are intrinsic to the group of participants, the nature of the addressed problems (topics), or a choice in the decision process design. We highlight the unique challenges facing manual group decision-making and those linked with intelligent support for group decision-making. Manual rules governing traditional face-to-face meetings must be adapted to suit virtual meetings where co-decisions must be made electronically. Virtual group decision-making for large groups facilitated by the extensive use of social media tools is an emergent area that has proven to be a necessity that has had a tremendous influence on society for the whole world during the recent Covid-19 pandemic. We first present the associated challenges, followed by the potential solution of computer-based group decision-making systems. We further discuss binary and multi-features of group decision approaches with justifications and examples. Communication has been identified as the main bottleneck, and various attacks have been revealed. This research opens up several aspects of group decision-making that could be further studied. For example, a cluster of dimensions concerns the truthfulness of the information exchanged and how untruthfulness is handled, starting with detection and how to react to detected lies. The scale of technology-supported group decision-making has grown to the point where its influence has been accused or lauded in the last few US elections. There is a persistent call for properties such as transparency and fairness in group decision-making systems.
DynO-IoT: a dynamic ontology for provisioning semantic interoperability in internet of things

A wide variety of devices and communication modules are emerging according to the specific requirements of various internet of things (IoT) applications. Provisioning semantic interoperability in this heterogeneous environment is a challenging task. Ontology provides a common platform for all the devices and applications available in a network. However, the existing ontologies are either very complex or unable to serve some specific requirements of IoT applications. Additionally, most of these are semantically static in nature. Addressing these concerns, we propose a semantically dynamic ontology for IoT, DynO-IoT which adapts to the static and dynamic IoT environments very easily. It supports all types of IoT devices and automatically adds new concepts to the ontology using machine learning techniques. The proposed model increases accuracy by 17% and reduces query response time by 35% approximately over the existing static ontologies.
The Application of Jigsaw Tasks by Using Scrambled Sentences in Teaching Reading Descriptive Text

This study aims to know the implementation of Jigsaw Tasks by using Scrambled Sentences in teaching reading descriptive text and its strengths and weaknesses. This is qualitative research. The data are collected using observation and interviews. Meanwhile, the data analysis uses three steps: reduction data, display data, and data verification. The results show that in the implementation of jigsaw tasks by using scrambled sentences, the students pay attention and listen to the explanation of the teacher during the teaching-learning process. They rearrange the jumble sentences card based on the pictures by adhering to the flannel board. The advantages are it could increase the student’s vocabulary mastery, and the jigsaw task technique with scramble sentences could motivate the students to understand the descriptive text and make the students focus on the material given. Besides, the students are active, enthusiastic, and enjoy learning to read descriptive text by using jigsaw tasks with scrambled sentences The disadvantages are the students make noise. Hence, his method is recommended for all teachers to use for teaching reading descriptive text and for future research to conduct the same research but in different fields.
A Novel Optical Encryption Strategy using Virtual Digital Holography and Fractional Fourier Based Key Hiding

This communication presents a novel image encryption strategy using on virtual optics, digital holography, phase shifting, and fractional Fourier transform (FRT) based key hiding. The sensitive information needed to be transferred was converted as a binary image and encrypted using Mach-Zehnder virtual holography. For recovering the phase information from the deformed hologram, decryption was performed. Towards this, four step wrapped phase recovery, and line scan based unwrapping strategy was used. For secure key transmission, FRT with two random phase masks along with tuning parameters were used. The results retrieved using simulation conclusively establish the validity of the proposed strategy and security against hack attacks. For analyzing the information accuracy of the strategy, root mean square error was calculated.