Reviewerly/exHarmony huggingface dataset:
Stability and Similarity Detection for the Biologically Inspired Temporal Pooler Algorithms

The biology of the brain can inspire the development of a wide range of intelligent information processing algorithms. Spatiotem-poral processing allows the brain to aggregate and represent information on multiple time and space scales. This paper examines temporal pooling, a biologically inspired spatiotemporal processing algorithm, in the task of creating stable sequential representations of objects while preserving their relative similarity. We compare two temporal pooling algorithm implementations: an existing Hierarchical Temporal Memory framework implementation called Union Temporal Pooler and our proposed Sandwich Temporal Pooler. We evaluate the temporal poolers on synthetic sequences using various aggregating metrics. It is shown that both implementations preserve high similarity; however, Union Temporal Pooler has better ability to preserve low and moderate similarity, while the proposed Sandwich Temporal Pooler features better output representation stability and computational efficiency.
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments

We present a novel reinforcement learning (RL) based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments. Our approach is designed for scenarios in which multiple robots are used to perform various pick up and delivery tasks. We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it. At the higher level, we solve the task allocation by formulating it in terms of Markov Decision Processes and choosing the appropriate rewards to minimize the Total Travel Delay (TTD). At the lower level, we use a decentralized navigation scheme based on ORCA that enables each robot to perform these tasks in an independent manner, and avoid collisions with other robots and dynamic obstacles. We combine these lower and upper levels by defining rewards for the higher level as the feedback from the lower level navigation algorithm. We perform extensive evaluation in complex warehouse layouts with large number of agents and highlight the benefits over state-of-the-art algorithms based on myopic pickup distance minimization and regret-based task selection. We observe improvement up to 14% in terms of task completion time and up-to 40% improvement in terms of computing collision-free trajectories for the robots.
Third Eye Hand Glove Object Detection for Visually Impaired using You Only Look Once (YOLO)v4-Tiny Algorithm

An estimate of 1.3 billion people suffers visual problems, which causes a lower quality of life because sight is recognized to be vital for humans and is core in assisting them in their day-to-day activities. The study proposes a system to develop a third eye-hand glove object detection for visually challenged people with the You Only Look Once (YOLO)v4-tiny algorithm that detects indoor objects. The system captures the image using the camera attached to the Raspberry Pi 4B will be fed to the system. The object detection process will then proceed to identify object types. Distance estimation comes afterward to calculate the distance of the identified object away from the camera, both outputted through an audio output. The system included forty (40) tests of the objects from the Common Objects in Context (COCO) dataset found indoors. The system's overall F1 score, precision, recall, and accuracy is 83.00%.
TikTok in Mobile-Assisted English Language Learning: An Exploratory Study

Embedded in interactive and participatory Web 2.0 applications, the complex experiences of English as a foreign language (EFL) learners in the digital age have triggered considerable interest in mobile-assisted language learning (MALL). Abundant research notwithstanding, much remains to be explored in MALL, leaving unknown the multifaceted effects of specific technological applications on various aspects of learning. Placed in China’s higher education context, the present mixed-methods study characterised by an exploratory sequential design intends to fill this gap by focusing on TikTok use in EFL learning. The initial qualitative study indicated EFL learners’ contrasting perceptions of TikTok use in formal learning, centring on its possible effects on global language proficiency, speaking proficiency and affective learning typified by motivation for general language learning, motivation to speak English volitionally and willingness to communicate. Further quantitative research demonstrated that MALL, wherein TikTok was used to supplement classroom-based teaching, did not exert the profound effects on language learning as suggested by the qualitative participants and in previous literature and that learners were less motivated for English learning and communication, though their extrinsic motivation in speaking English increased. Despite the contradiction between these findings with previous ones, this preliminary study is assumed to be significant as it necessitates a critical attitude towards MALL in general and TikTok-assisted EFL learning in particular, encouraging researchers to re-examine these issues in different socio-educational contexts and urging educators to ponder whether it is sensible to implement MALL with the assistance of digital applications, and if so, how to make it as efficacious as anticipated.
Asynchronous Semi-Supervised Federated Learning with Provable Convergence in Edge Computing

Traditional federated learning methods assume that users have fully labeled data in their device for training, but in practice, labels are difficult to obtain due to various reasons such as user privacy concerns, high labeling costs, and lack of expertise. Semi-supervised learning has been introduced into federated learning scenarios to address the lack of labels, but performance suffers from slow training and non-convergence in real network environments. In this article, we propose Federated Incremental Learning (FedIL) as a semi-supervised federated learning (SSFL) framework in edge computing to overcome the limitations of SSFL. FedIL introduces a group-based asynchronous training algorithm with provable convergence, which accelerates model training by allowing more clients to participate simultaneously. We developed a prototype system and performed track-driven simulations to demonstrate FedIL's superior performance.
Semantic-Based Design of an Adaptive User Interface

An approach to the building of adaptive user interfaces (UIs) based on their semantic model (semantic-based design) is described in the article. A variant of the implementation of a framework for building UIs is proposed within this approach, which is based on the OSTIS Technology. The existing approaches to the building of UIs are also considered; examples and methods of designing UI components for the proposed implementation variation are shown.
SVIP: Sequence VerIfication for Procedures in Videos

In this paper, we propose a novel sequence verification task that aims to distinguish positive video pairs performing the same action sequence from negative ones with step-level transformations but still conducting the same task. Such a challenging task resides in an open-set setting without prior action detection or segmentation that requires event-level or even frame-level annotations. To that end, we carefully reorganize two publicly available action-related datasets with step-procedure-task structure. To fully investigate the effectiveness of any method, we collect a scripted video dataset enumerating all kinds of step-level transformations in chemical experiments. Besides, a novel evaluation metric Weighted Distance Ratio is introduced to ensure equivalence for different step-level transformations during evaluation. In the end, a simple but effective baseline based on the transformer encoder with a novel sequence alignment loss is introduced to better characterize long-term dependency between steps, which outperforms other action recognition methods. Codes and data will be released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> :
The mediating effect of job satisfaction on the relationship between the delegation of authority and the performance of employees

The current study aims to examine the relationship between the delegation of authority and employee performance in the banking sector in Palestine. Delegating authority explores new competencies and experiences that increase the quantity and quality of performance (Al-Jammal, Al-Khasawneh, &amp; Hamadat, 2015). The concept of delegation of authority is gaining increasing attention by human resources management in large organizations because the concept of delegation of authority is one of the most important concepts used in the practice of management (Muhammad &amp; Kazmi, 2020). A questionnaire was used to collect data and 300 questionnaires were distributed after being reviewed by a panel of experts. The Social Package for the Social Sciences (SPSS) was used to analyze and process the data, and the study found that there is a positive relationship between the delegation of authority and performance. This relationship mediates job satisfaction, as delegation contributes to increased job satisfaction, thus, job satisfaction increases the quality of performance. The current research recommended that additional efforts should be invested to enhance the culture of delegation of power and attention to the state of job satisfaction among workers in banks as well as other institutions and that future research should focus on finding new ways to ensure improvement in the quality of performance in the banking sector in Palestine
Transferring Image-CLIP to Video-Text Retrieval via Temporal Relations

We present a novel network to transfer the image-language pre-trained model to video-text retrieval in an end-to-end manner. Leading approaches in the domain of video-and-language learning try to distill the spatio-temporal video features and multi-modal interaction between videos and language from a large-scale video-text dataset. Differently, we leverage the pre-trained image-language model, and simplify it as a two-stage framework including co-learning of image and text, and enhancing temporal relations between video frames and video-text respectively. Specifically, based on the spatial semantics captured by Contrastive Language-Image Pre-training (CLIP) model, our model involves a Temporal Difference Block (TDB) to capture motions at fine temporal video frames, and a Temporal Alignment Block (TAB) to re-align the tokens of video clips and phrases and enhance the cross-modal correlation. These two temporal blocks efficiently realize video-language learning and enable the proposed model to scale well on comparatively small datasets. We conduct extensive experimental studies including ablation studies and comparisons with existing SOTA methods, and our proposed approach outperforms them on the popularly-employed text-to-video and video-to-text retrieval benchmarks, including MSR-VTT, MSVD, LSMDC, and VATEX.
A Framework for Design, Verification, and Management of SoC Access Control Systems

System-on-chip (SoC) architectures are a heterogeneous mix of microprocessors, custom accelerators, memories, interfaces, peripherals, and other resources. These resources communicate using complex on-chip interconnect networks that attempt to quickly and efficiently arbitrate memory transactions whose behaviors can vary drastically depending on the current mode of operation and system operating state. Security- and safety-critical applications require access control policies that define how these resources interact to ensure that malicious and unsafe behaviors do not occur. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> is a design and verification framework for on-chip access control. The core of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> is the access control wrapper (ACW)–a high-performance yet efficient hardware module that dynamically arbitrates on-chip communications. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> distributes ACWs across the SoC and programs them to perform local access control. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> provides a firmware generation tool and a property-driven security verification methodology to ensure that the ACWs are properly integrated and configured. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> security verification confirms that the ACW behaves properly at IP level. It verifies the hardware root of trust firmware configures the ACW correctly. And it evaluates system-level security threats due to interactions between shared resources. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> is experimentally validated on a Xilinx UltraScale+ programmable SoC. Additionally, an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aker</small> access control system is integrated into the OpenPULP multicore archtiecture that uses OpenTitan hardware root-of-trust for firmware configuration.
Controllable Video Generation Through Global and Local Motion Dynamics

We present GLASS, a method for Global and Local Action-driven Sequence Synthesis. GLASS is a generative model that is trained on video sequences in an unsupervised manner and that can animate an input image at test time. The method learns to segment frames into foreground-background layers and to generate transitions of the foregrounds over time through a global and local action representation. Global actions are explicitly related to 2D shifts, while local actions are instead related to (both geometric and photometric) local deformations. GLASS uses a recurrent neural network to transition between frames and is trained through a reconstruction loss. We also introduce W-Sprites (Walking Sprites), a novel synthetic dataset with a predefined action space. We evaluate our method on both W-Sprites and real datasets, and find that GLASS is able to generate realistic video sequences from a single input image and to successfully learn a more advanced action space than in prior work. Further details, the code and example videos are available at https://araachie.github.io/glass/ .
Modeling of Noisy Quantum Circuits using Random Matrix Theory

A major challenge in exploiting the principles of quantum information is the influence of noise which tends to work against the quantum features of a system. The traditional quantum gate error model provides a general framework to study noisy quantum circuits, but may fail to capture intricate microscopic interactions between qubits in addition to the environment. This can significantly overestimate the feasibility of implementing useful quantum algorithms on physical quantum hardware. In this paper, we study a noise model driven by random matrix theory that captures an effective microscopic interaction, and assess the suitability of the quantum gate error model. Our approach assumes noise arises due to interactions with many physical sources, and modeled using random matrix theory and applied to the quantum circuit model.
Weakly Supervised Grounding for VQA in Vision-Language Transformers

Transformers for visual-language representation learning have been getting a lot of interest and shown tremendous performance on visual question answering (VQA) and grounding. However, most systems that show good performance of those tasks still rely on pre-trained object detectors during training, which limits their applicability to the object classes available for those detectors. To mitigate this limitation, this paper focuses on the problem of weakly supervised grounding in the context of visual question answering in transformers. Our approach leverages capsules by transforming each visual token into a capsule representation in the visual encoder; it then uses activations from language self-attention layers as a text-guided selection module to mask those capsules before they are forwarded to the next layer. We evaluate our approach on the challenging GQA as well as VQA-HAT dataset for VQA grounding. Our experiments show that: while removing the information of masked objects from standard transformer architectures leads to a significant drop in performance, the integration of capsules significantly improves the grounding ability of such systems and provides new state-of-the-art results compared to other approaches in the field. (Code is available at https://github.com/aurooj/WSG-VQA-VLTransformers)
Image Quality Score Distribution Prediction via Alpha Stable Model

Based on potentially subjective and diverse image quality scores given by a group of subjects, we propose to predict the distribution of image quality scores rather than the mean opinion score (MOS) of image quality. Therefore, in this paper, we use an alpha stable model to parameterize the image quality score distribution (IQSD), and propose an objective method to predict the alpha-stable-model-based IQSD. First, the LIVE database is re-recorded. Specifically, we invite a large group of subjects (187 valid subjects) to evaluate the quality of all 808 images in the LIVE database, with their scores forming reliable IQSDs. All images in the LIVE database and their collected subjective quality scores form a new image quality assessment database, named the SJTU IQSD database. We then propose a framework and algorithm to predict the alpha-stable-model-based IQSD, in which quality features are extracted from the structural and natural statistical information of each image, and support vector regressors are trained to predict the alpha stable model parameters. Experiments carried out on the SJTU IQSD database verify the feasibility of using the alpha stable model to describe the IQSD, and the experimental results show that the alpha-stable-model-based IQSD can reflect a large amount of subjective information on image quality. We also prove that the objective alpha-stable-model-based IQSD prediction method is effective. The code and the SJTU IQSD database can be downloaded at ‘ <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YixuanGao98/Image-Quality-Score-Distribution-Prediction-via-Alpha-Stable-Model.git</uri> ’.
SMARTREC: A Conversational Recommendation System Using Semantic Knowledge Graphs

Conversational Recommendation Systems (CRSes) are emerging as the next-generation enabler of e-commerce services. Conversational interactions enable an interactive and engaging experience for the user and provide personalization capabilities unmatched in traditional recommendation system scenarios. In this paper, we propose a novel real-time CRS based on an intelligent domain knowledge graph with semantic network extensions, and demonstrate its effectiveness, scalability, and real-time performance relative to state-of-the-art approaches. We utilize a novel vacation booking use case, which we illustrate using a large Airbnb dataset, a conversational Artificial Intelligence (AI) component based on the open-source RASA [1] framework, and commercial-grade Neo4j [2] knowledge graph and graph-based recommendation algorithms.
Adaptive Transfer Kernel Learning for Transfer Gaussian Process Regression

Transfer regression is a practical and challenging problem with important applications in various domains, such as engineering design and localization. Capturing the relatedness of different domains is the key of adaptive knowledge transfer. In this paper, we investigate an effective way of explicitly modelling domain relatedness through transfer kernel, a transfer-specified kernel that considers domain information in the covariance calculation. Specifically, we first give the formal definition of transfer kernel, and introduce three basic general forms that well cover existing related works. To cope with the limitations of the basic forms in handling complex real-world data, we further propose two advanced forms. Corresponding instantiations of the two forms are developed, namely Trkαβ and Trkω based on multiple kernel learning and neural networks, respectively. For each instantiation, we present a condition with which the positive semi-definiteness is guaranteed and a semantic meaning is interpreted to the learned domain relatedness. Moreover, the condition can be easily used in the learning of TrGP αβ and TrGP ω that are the Gaussian process models with the transfer kernels Trkαβ and Trkω respectively. Extensive empirical studies show the effectiveness of TrGP αβ and TrGP ω on domain relatedness modelling and transfer adaptiveness.
Grasp Planning for Occluded Objects in a Confined Space with Lateral View Using Monte Carlo Tree Search

In the lateral access environment, the robot be-havior should be planned considering surrounding objects and obstacles because object observation directions and approach angles are limited. To safely retrieve a partially occluded target object in these environments, we have to relocate objects using prehensile actions to create a collision-free path for the target. We propose a learning-based method for object rearrangement planning applicable to objects of various types and sizes in the lateral environment. We plan the optimal rearrangement sequence by considering both collisions and approach angles at which objects can be grasped. The proposed method finds the grasping order through Monte Carlo tree search, significantly reducing the tree search cost using point cloud states. In the experiment, the proposed method shows the best and most stable performance in various scenarios compared to the existing TAMP methods. In addition, we confirm that the proposed method trained in simulation can be easily applied to a real robot without additional fine-tuning, showing the robustness of the proposed method.
The Effects of Random Undersampling for Big Data Medicare Fraud Detection

We show it is possible to obtain better classification performance for experiments involving highly imbalanced Big Data with the application of data sampling techniques. We apply Random Undersampling to a publicly available Medicare insurance claims dataset of about 175 million records. We find that Random Undersampling significantly improves the classification performance of Extremely Randomized Trees and XGBoost learners in a Medicare Big Data fraud classification task. We employ Random Undersampling to evaluate performance at multiple minority:majority class ratios. According to the outcome of a Tukey’s Honestly Significant Difference test, we find Random Undersampling to the 1:9 or 1:27 class ratios yields the best performance, providing Area Under the Receiver Operating Characteristic scores of over 0.97. Models built with undersampled Big Data require significantly less time to train. Our contribution is to prove the effectiveness of Random Undersampling in classifying Medicare Big Data. Our review of related work shows we are the first to apply Random Undersampling to data on this scale in order to prove one can obtain better performance. To the best of our knowledge, we are the first to perform experiments with the latest Medicare Part D data, made available in 2021.