Reviewerly/exHarmony huggingface dataset:
DHTS: A Dynamic Hybrid Tiling Strategy for Optimizing Stencil Computation on GPUs

Stencil computation is an important class of computational modes in scientific computing applications. Loop tiling techniques have been widely studied to accelerate stencil computations on different architectures by exploiting parallelism and data locality. Recent advanced tiling methods enable the tile-wise concurrent start-up to improve the execution performance. However, such methods statically partition all dimensions of iteration space into tiles with predetermined complex shapes and sizes, and thus lead to low thread utilization and memory access efficiency on GPUs. In this paper, we present DHTS, a novel dynamic hybrid tiling strategy for stencil computations. DHTS employs static tiling on the outer dimensions to achieve concurrent start-up parallelism, while proposes a dynamic rectangular tiling method on the inner dimensions to improve thread utilization and memory access efficiency. By deriving tile size constraints, DHTS adaptively achieves equal-size workload of tiles, and therefore reducing idle threads and increasing coalesced memory accesses within tiles. We implement the proposed strategy with different complex tile shapes. Experimental results on Titan V and Tesla V100 GPUs show that DHTS effectively improves the execution performance of 2D/3D stencils compared to state-of-the-art tiling methods, and achieves the best improvement of 28×.
SOLUTION OF ONE-PARAMETER GENERALIZED PALINDROMIC PROBLEMS

The issues on applying a computer program created on the language "Python" on the basis of the developed methods for solving one-parameter generalized palindromic problems are considered. The results of solving one model example are presented, and the computational efficiency of the developed tools is shown.
Building Conventional “Experts” With a Dialogue Logic Programming Language

We describe DiaProlog, a dialogue logic programming language that extends the vanilla Prolog with the features to facilitate the integration of reasoning capabilities into task-oriented dialogue systems. The extended language combines the expressive power of Horn rule and description logic with uncertainty and allows us to describe the specifications for both problem solving and dialogue management in a declarative programming manner. The systems incorporated with DiaProlog are capable of asking appropriate questions when necessary and collecting the answers to direct the line of reasoning and guide the conversation toward the correct solution. Besides, an explanation facility is provided to explain the reasoning behind its conclusion. We also describe a dialogue management framework built upon DiaProlog, which has been validated in multiple implementations providing legal consulting, financial advising, and medical guidance services where many questions require complex inferences about knowledge.
Interdependence of sound standards

Sound standards are developed by multiple organizations, which rely on one another to build complex dependencies. These interdependencies are explored through the example of using ASA/ASNI S12.60 in a hypothetical application of quiet classroom design. The dependencies are contextualized through multiple viewpoints to better understand how each user has their own hierarchy of standards prioritization while benefiting from the ability to trust the infrastructure provided by standards outside their direct area of interest. Finally, the prioritization hierarchy for each point of view is compared to demonstrate why different users might choose to engage in standards development through different organizations.
Joint Path Alignment Framework for 3D Human Pose and Shape Estimation From Video

3D human pose and shape estimation (3D-HPSE) from video aims to generate sequence of 3D mesh that depict human body in the video. Current deep learning based 3D-HPSE networks that takes video input have focused on improving temporal consistency among sequence of 3D joints by supervising acceleration error between predicted and ground-truth human motion. However, these methods overlooked the geometric misalignments of persistent discrepancy between geometric paths drawn by sequence of predicted joints and that of ground-truth joints. To this end, we propose Joint Path Alignment (JPA) framework, a model-agnostic approach that mitigates geometric misalignments by introducing Temporal Procrustes Alignment Regularization (TPAR) loss that performs group-wise sequence learning of joint movement paths. Unlike previous methods that rely solely on per-frame supervision for accuracy, our framework adds sequence-level accuracy supervision with TPAR loss by performing Procrustes analysis on the geometric paths drawn by sequences of predicted joints. Our experiments show that JPA framework advances the network to exceed the previous state-of-the-art performances on benchmark datasets in both per-frame accuracy and video smoothness metric.
Joint Deep Estimation of Intrinsic and Dichromatic Image Decomposition

This paper proposes an image formation model that jointly combines dichromatic and intrinsic image decomposition models. The two decomposition models analyze image formation process from a different perspective, and they can be combined synergistically. It is confirmed that the proposed method performs better than the individual decomposition. The joint estimation and the study of the decomposition order (‘intrinsic + dichromatic’ or ‘dichromatic + intrinsic’) are the first attempt to the best of our knowledge. It was confirmed that the proposed ‘intrinsic + dichromatic’ is more optimal through experimental evaluations. We also exploit the temporal property of AC light sources, which can further improve the decomposition performance. The experimental results show that the proposed model can make an accurate image decomposition and achieve a remarkable color constancy performance.
Task-Oriented Explainable Semantic Communications

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the β-variational autoencoder (β-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.
Youtube Engagement Analytics via Deep Multimodal Fusion Model

As popularity of video-sharing platforms, content creators have a high demand to produce content which attracts the large amount of viewers. There are many factors related to engagement: visual, sound, transcript, title etc. To take into account of these factors, we propose a deep multi-modal hybrid fusion for YouTube video engagement. Our architecture allows us to be easy to adapt state-of-the-art models for a particular task or variety of modalities, then fuse them to obtain more information aim to classify better. A proposed residual block as a simple neuron architecture search is used to get better features extracted. Our work is at the forefront of classifying YouTube video engagement and promises to broaden the research community’s reach. Through detailed experiments, we proved that the model is the state-of-the-art in problem YouTube video engagement analytics.
Arbitrary Message Passing Across Blockchains

This paper investigates an emerging topic in a multi-blockchain world: the arbitrary message passing (AMP) problem, that is, roughly speaking, how to transmit general information from one blockchain to another. We point out that AMP protocols naturally generalize asset bridges and are special types of oracles. We develop a general framework for AMP and distill the defining properties of AMP solutions. We then apply the framework to analyze several representative AMP protocols developed in practice, explain their mechanisms, and discuss their respective security properties as well as performance.
Inhaler Motion Evaluation Via Weighted DP Matching

Patients diagnosed with bronchial asthma or chronic obstructive pulmonary disease (COPD) are required to use in- halers to control their symptoms. However, approximately 70 % of the patients use their inhalers incorrectly because of misunderstandings regarding their use or habituation owing to long-term use. Currently, in-person instructions provided by pharmacists or doctors are the only remedy to this problem. Therefore, new evaluation method, which eliminates the input of a doctor or pharmacist, is urgently needed. This study aims to realize automatic evaluation of individual inhaler motion. A new sensing attachment with inertial measurement unit (IMU) is developed for an inhaler device. We propose an evaluation method using data from the sensing attachment, without doctor or pharmacist issued instructions. The proposed evaluation method for inhaler motion is based on a dynamic programming (DP) matching algorithm. Furthermore, the evaluation method is verified via suitable clinical experiments. Based on the experimental results, all clinical data could be classified by the proposed method.
DESEM: Depthwise Separable Convolution-Based Multimodal Deep Learning for In-Game Action Anticipation

In real-time strategy (RTS) games, to defeat their opponents, players need to choose and implement the correct sequential actions. Because RTS games like StarCraft II are real-time, players have a very limited time to choose how to develop their strategy. In addition, players can only partially observe the parts of the map that they have explored. Therefore, unlike Chess or Go, players do not know what their opponents are doing. For these reasons, applying generally used artificial intelligence models to forecast sequential actions in RTS games is a challenge. To address this, we propose depthwise separable convolution-based multimodal deep learning (DESEM) for forecasting sequential actions in the game StarCraft II. DESEM performs multimodal learning using high-dimensional frames and action labels simultaneously as inputs. We use a depthwise separable convolution as the backbone network for extracting features from high-dimensional frames. In addition, we propose a weighted loss function to resolve class imbalances. We use 1,978 StarCraft II replays where the Terrans win in a Terran vs. Protoss game. The experimental results show that the proposed depthwise separable convolution is superior to the conventional convolution. Furthermore, we demonstrate that multimodal learning and the weighted loss function contribute significantly to improving forecasting performance.
Vadalog: Overview, Extensions and Business Applications

Knowledge graphs (KGs) have in recent years gained a large momentum both in academic research and in business applications. They have become a bridge between databases, artificial intelligence (AI), data science, the (semantic) web, linked data, and many other areas. In particular, in declarative AI, they have become a bridge between logic-based reasoning, and machine learning-based reasoning. Languages for KGs on the one hand, and systems for KGs – i.e., Knowledge Graph Managament System (KGMS) – on the other hand, have garnered increasing attention. Of particular importance are language and system extensions – such as probabilistic reasoning, numeric reasoning, etc. – supporting various real-world applications, and the business applications that can be built using such extensions. In this work, we give an overview of the Vadalog language and system, a KGMS. We focus on three areas: (1) a basic overview, including an introduction to dependencies, the Datalog and Vadalog languages, (2) the extensions of the system, including arithmetic and aggregation, real-world data interfaces, temporal reasoning, and machine learning, and (3) the business applications, including: corporate governance, media intelligence, supply chains, collateral eligibility, hostile takeovers, smart anonymization, and anti-money laundering.
Aircraft Tracking Based on an Antidrift Multifilter Tracker in Satellite Video Data

Using remote sensing video to monitor aircraft dynamics is significant for military applications, airport management, and aircraft rescue.The aircraft has a fixed size and obvious characteristics, so it is suitable for correlation filtering.Correlation filtering algorithms can extract features from input data to predict motion trajectories, and the calculation speed of correlation filterings is fast.Hence, such algorithms are advantageous for tracking targets in remote sensing images.In this article, an antidrift multifilter tracker based on a correlation filter and the Kalman filter is proposed for this purpose.This article proposes a temporal consistency-constrained background-aware correlation filter algorithm based on temporal regularization that resists the model drift caused by clouds by using motion information to correct it.Experimental results show that our proposed method shows improved antidrift performance compared with other advanced tracking methods in cases of cloud occlusion and stable performance in other complex conditions.We believe that our model will be helpful for researchers who are interested in object tracking in satellite video, especially for processing satellite video data with cloud occlusion.
CBSS: A New Approach for Multiagent Combinatorial Path Finding

Conventional multiagent path finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to preallocated destinations. This article considers a generalized version of MAPF called multiagent combinatorial path finding, where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collision-free paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e., target sequencing). To solve the problem, we leverage conflict-based search (CBS) for MAPF and propose a novel approach called conflict-based Steiner search (CBSS). CBSS interleaves 1) the collision resolution strategy in CBS to bypass the curse of dimensionality in MAPF and 2) multiple traveling salesman algorithms to handle the combinatorics in target sequencing, to compute optimal or bounded suboptimal paths for agents while visiting all the targets. We also develop two variants of CBSS that trade off runtime against solution optimality. Our test results verify the advantage of CBSS over the baselines in terms of computing cheaper paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. Finally, we run both Gazebo simulation and physical robot tests to validate that the planned paths are executable.
Finding Materialized Models for Model Reuse

Materialized model query aims to find the most appropriate materialized model as the initial model for model reuse. It is the precondition of model reuse, and has recently attracted much attention. Nonetheless, the existing methods suffer from the need to provide source data, limited range of applications, and inefficiency since they do not construct a suitable metric to measure the target-related knowledge of materialized models. To address this, we present <inline-formula><tex-math notation="LaTeX">${\sf MMQ}$</tex-math></inline-formula> , a source-data free, general, efficient, and effective materialized model query framework. It uses a Gaussian mixture-based metric called separation degree to rank materialized models. For each materialized model, <inline-formula><tex-math notation="LaTeX">${\sf MMQ}$</tex-math></inline-formula> first vectorizes the samples in the target dataset into probability vectors by directly applying this model, then utilizes Gaussian distribution to fit for each class of probability vectors, and finally uses separation degree on the Gaussian distributions to measure the target-related knowledge of the materialized model. Moreover, we propose an improved <inline-formula><tex-math notation="LaTeX">${\sf MMQ}$</tex-math></inline-formula> ( <inline-formula><tex-math notation="LaTeX">${\sf I\text{-}MMQ}$</tex-math></inline-formula> ), which significantly reduces the query time while retaining the query performance of <inline-formula><tex-math notation="LaTeX">${\sf MMQ}$</tex-math></inline-formula> . Extensive experiments on a range of practical model reuse workloads demonstrate the effectiveness and efficiency of <inline-formula><tex-math notation="LaTeX">${\sf MMQ}$</tex-math></inline-formula> .
A Federated Learning Approach for Text Classification Using NLP

Text classification is important in many aspects of natural language processing (NLP), such as word semantic categorization, emotion analysis, question answering, and conversation management. Law, health, and marketing are just a few of the professions that have made use of it throughout the last century. We focused on emotion analysis in this research, which includes categories like happiness, sadness, and more. We investigated the precision of three alternative models for assessing the emotional tone of written text. Deep learning models like GRU (Gated Recurrent Unit) and CNN (Convolutional Neural Network) are used in conjunction with the Bi-LSTM (Bidirectional LSTM) model. These three major deep learning architectures have been extensively studied for classification applications. Finally, the model with the greatest accuracy on the dataset was trained using federated learning (FL). Using the FL approach, more data has to be collected, eliminating data gaps and ensuring data security. The focus of this research is to increase the model’s accuracy by collaborating with FL approaches while maintaining data confidentiality.
Prefix Coding Scheme Supporting Direct Access Without Auxiliary Space

Entropy coding is a widely used technique for lossless data compression. The entropy coding schemes supporting the direct access capability on the encoded stream have been investigated in recent years. However, all prior schemes require auxiliary space to support the direct access ability. This paper proposes a rearranging method for prefix codes to support a certain level of direct access to the encoded stream without requiring additional data space. Then, an efficient decoding algorithm is proposed based on lookup tables. The simulation results show that when the encoded stream does not allow additional space, the number of bits per access read of the proposed method is above two orders of magnitude less than the conventional method. In contrast, the alternative solution consumes at least one more bit per symbol on average than the proposed method to support direct access. This indicates that the proposed scheme can achieve a good trade-off between space usage and access performance. In addition, if a small amount of additional storage space is allowed (it is approximately 0.057% in the simulation), the number of bits per access read in our proposal can be significantly reduced by 90%.
A Hybridized Teaching–Learning-Based Optimization Algorithm to Solve Capacitated Vehicle Routing Problem

Nature-inspired algorithms are inspired by the assorted processes that are realized in nature. These approaches are divided into different categories like swarm-based optimization approaches, evolutionary approaches, physical-inspired approaches, bio-inspired approaches, and so on. Swarm intelligence techniques are a collective behavior of a decentralized or self-organized system. These systems consist of numerous individuals with limited intelligence interacting with each other. Teaching–learning-based optimization (TLBO) technique could be a populace-based meta-heuristic technique made up of two phases, namely, teacher phase and learner phase. This algorithm depicts the classroom teaching methodology. After the inception of TLBO, several researchers have been continuously working to refine the performance of the algorithm. This article also permits an hybridized TLBO algorithm for a class of vehicle routing problem. A new phase inspired from elephant herding optimization (EHO) is incorporated with TLBO. The hybridized algorithm is entitled as hybridized teaching–learning-based optimization algorithm. The modified algorithm is applied to clarify a class of capacitated vehicle routing problem. The captured results validate the authenticity of the proposed technique.
Anomaly Detection in the Course Evaluation Process

The power of analytics, which extracts useful and actionable information from routinely collected data sets, for the self-improvement of an organization. The emergence of learning analytics and educational data mining will help in monitoring, measuring, and controlling learning interactions and learning environments, bringing in improvisations and innovations in the educational field. Continuous course assessments are learning interactions, where the student can demonstrate his acquired skills and knowledge. These evaluations need to be critically analyzed and controlled. In this paper, an analytical model is proposed for the course evaluation process to demonstrate the use of actionable analytics on data collected as a part of the admission and evaluation process and detecting anomalies in the evaluation process. Techniques ranging from supervised to unsupervised learning and some statistical tools are applied to student performance data for pinpointing discrepancies in the evaluation process. Violin plots are used to depict the comparison of a variable distribution across varied categories. Statistical summary to determine the conversion rate variations. Isolation trees in which the random data is put through and processed in a tree format. K-nearest neighbor known as k-nn in which the classification of data provides details of the chance of data point being a member of a specific group based on the proximity to the data point. Cluster analysis is for the exploratory data analysis and is a way to solve the classification methods. Confusion matrix was used to explain the algorithm. Probability density functions are used to measure the outcome of the discrete value.