Reviewerly/exHarmony huggingface dataset:
Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices

Artificial intelligence (A.I.) has revolutionised a wide range of human activities, including the accelerated development of autonomous vehicles. Self-navigating delivery robots are recent trends in A.I. applications such as multitarget object detection, image classification, and segmentation to tackle sociotechnical challenges, including the development of autonomous driving vehicles, surveillance systems, intelligent transportation, and smart traffic monitoring systems. In recent years, object detection and its deployment on embedded edge devices have seen a rise in interest compared to other perception tasks. Embedded edge devices have limited computing power, which impedes the deployment of efficient detection algorithms in resource-constrained environments. To improve on-board computational latency, edge devices often sacrifice performance, creating the need for highly efficient A.I. models. This research examines existing loss metrics and their weaknesses, and proposes an improved loss metric that can address the bounding box regression problem. Enhanced metrics were implemented in an ultraefficient YOLOv5 network and tested on the targeted datasets. The latest version of the PyTorch framework was incorporated in model development. The model was further deployed using the ROS 2 framework running on NVIDIA Jetson Xavier NX, an embedded development platform, to conduct the experiment in real time.
KGVQL: A knowledge graph visual query language with bidirectional transformations

With the rapid development of artificial intelligence, knowledge graphs have been widely recognized as a critical component in many AI techniques and systems. A complex knowledge graph may contain hundreds of millions of nodes and edges, thus is challenging for end-users to understand and query. In this paper, we present a knowledge graph interactive visual query language, KGVQL, to improve the efficiency of end-users’ understanding and querying of knowledge graphs. Furthermore, KGVQL realizes the novel capability of flexible bidirectional transformations between query graphs and query results, therefore significantly assisting end-users in constructing queries over large and unfamiliar knowledge graphs in an incremental way. We present the visual syntax of KGVQL, discuss our design rationale behind this interactive visual query language, and illustrate a number of case studies. We empirically evaluate the effectiveness of a visual query system based on KGVQL against a number of textual and visual query environments over a large knowledge graph, DBpedia. Our evaluation demonstrates the superiority of KGVQL in effectiveness and accuracy.
ConTra Corona: Contact Tracing against the Coronavirus by Bridging the Centralized–Decentralized Divide for Stronger Privacy

Contact tracing is among the most important interventions to mitigate the spread of any pandemic, usually in the form of manual contact tracing. Smartphone-facilitated digital contact tracing may help to increase tracing capabilities and extend the coverage to those contacts one does not know in person. Most implemented protocols use local Bluetooth Low Energy (BLE) communication to detect contagion-relevant proximity, together with cryptographic protections, as necessary to improve the privacy of the users of such a system. However, current decentralized protocols, including DP3T [T+20], do not sufficiently protect infected users from having their status revealed to their contacts, which raises fear of stigmatization. We alleviate this by proposing a new and practical solution with stronger privacy guarantees against active adversaries. It is based on the upload-what-you-observed paradigm, includes a separation of duties on the server side, and a mechanism to ensure that users cannot deduce which encounter caused a warning with high time resolution. Finally, we present a simulation-based security notion of digital contact tracing in the real–ideal setting, and prove the security of our protocol in this framework.
Long short-term enhanced memory for sequential recommendation

Abstract Sequential recommendation is a stream of studies on recommender systems, which focuses on predicting the next item a user interacts with by modeling the dynamic sequence of user-item interactions. Since being born to explore the dynamic tendency of variable-length temporal sequence, Recurrent Neural Networks (RNNs) have been paid much attention in this area. However, the inherent defects caused by the network structure of RNNs have limited their applications in sequential recommendation, which are mainly shown on two factors: RNNs tend to make point-wise predictions and ignore the collective dependencies because the temporal relationships between items change monotonically; RNNs are likely to forget the essential information during processing long sequences. To solve these problems, researchers have done much work to enhance the memory mechanism of RNNs. However, although previous RNN-based methods have achieved promising performance by taking advantage of external knowledge with other advanced techniques, the improvement of the intrinsic property of existing RNNs has not been explored, which is still challenging. Therefore, in this work, we propose a novel architecture based on Long Short-Term Memories (LSTMs), a broadly-used variant of RNNs, specific for sequential recommendation, called Long Short-Term enhanced Memory (LSTeM) , which boosts the memory mechanism of original LSTMs in two ways. Firstly, we design a new structure of gates in LSTMs by introducing a “Q-K-V” triplet, a mechanism to accurately and properly model the correlation between the current item and the user’s historical behaviors at each time step. Secondly, we propose a “recover gate” to remedy the inadequacy of memory caused by the forgetting mechanism, which works with a dynamic global memory embedding. Extensive experiments have demonstrated that LSTeM achieves comparable performance to the state-of-the-art methods on the challenging datasets for sequential recommendation.
Class-Specific Semantic Reconstruction for Open Set Recognition

Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existing methods based on auto-encoder (AE) and prototype learning show great potential in handling this challenging task. In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning. Specifically, CSSR replaces prototype points with manifolds represented by class-specific AEs. Unlike conventional prototype-based methods, CSSR models each known class on an individual AE manifold, and measures class belongingness through AE's reconstruction error. Class-specific AEs are plugged into the top of the DNN backbone and reconstruct the semantic representations learned by the DNN instead of the raw image. Through end-to-end learning, the DNN and the AEs boost each other to learn both discriminative and representative information. The results of experiments conducted on multiple datasets show that the proposed method achieves outstanding performance in both close and open set recognition and is sufficiently simple and flexible to incorporate into existing frameworks.
Leveraging Sound and Wrist Motion to Detect Activities of Daily Living with Commodity Smartwatches

Automatically recognizing a broad spectrum of human activities is key to realizing many compelling applications in health, personal assistance, human-computer interaction and smart environments. However, in real-world settings, approaches to human action perception have been largely constrained to detecting mobility states, e.g., walking, running, standing. In this work, we explore the use of inertial-acoustic sensing provided by off-the-shelf commodity smartwatches for detecting activities of daily living (ADLs). We conduct a semi-naturalistic study with a diverse set of 15 participants in their own homes and show that acoustic and inertial sensor data can be combined to recognize 23 activities such as writing, cooking, and cleaning with high accuracy. We further conduct a completely in-the-wild study with 5 participants to better evaluate the feasibility of our system in practical unconstrained scenarios. We comprehensively studied various baseline machine learning and deep learning models with three different fusion strategies, demonstrating the benefit of combining inertial and acoustic data for ADL recognition. Our analysis underscores the feasibility of high-performing recognition of daily activities using inertial-acoustic data from practical off-the-shelf wrist-worn devices while also uncovering challenges faced in unconstrained settings. We encourage researchers to use our public dataset to further push the boundary of ADL recognition in-the-wild.
Chowlk: from UML-Based Ontology Conceptualizations to OWL

Ontology conceptualization is an ontology development task that consists in generating a preliminary model based on the requirements that the ontology should represent. This activity is often carried out by generating the models as diagrams in a blackboard, paper or digital tools. The generated models drive the ontology implementation activity, where the model is formalized and completed using an implementation language. Normally, the ontology conceptualization output serves as guidance for the ontology implementation; however, ontology implementation is usually done from scratch using ontology editors. The goal of this work is to consider ontology conceptualizations as first-order artifacts in ontology development in order to boost the ontology implementation activity. For doing so we present Chowlk, a framework to transform digital machine-processable ontology conceptualization diagrams into OWL. Domain experts and ontologists benefit from this approach in several ways: 1) reduce time generating the first versions of the OWL file that can be invested on 2) focusing on the conceptualization diagrams that can be used both for 3) improving communication between ontology users and developers and 4) be reused during the ontology documentation stage.
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems

Warning: this paper contains examples that may be offensive or upsetting.The social impact of natural language processing and its applications has received increasing attention.In this position paper, we focus on the problem of safety for end-to-end conversational AI.We survey the problem landscape therein, introducing a taxonomy of three observed phenomena: the INSTIGATOR, YEA-SAYER, and IMPOSTOR effects.We then empirically assess the extent to which current tools can measure these effects and current systems display them.We release these tools as part of a "first aid kit" (SAFETYKIT) to quickly assess apparent safety concerns.Our results show that, while current tools are able to provide an estimate of the relative safety of systems in various settings, they still have several shortcomings.We suggest several future directions and discuss ethical considerations.
Further Stability Criteria for Sampled-Data-Based Interval Type-2 Fuzzy Systems via a Refined Two-Side Looped-Functional Method

This article investigates the stability and stabilization problem of aperiodic sampled-data nonlinear systems in the framework of interval type-2 (IT-2) fuzzy models. First, by introducing two adjustable parameters and splitting the sampling intervals into four nonuniform intervals, a refined two-side looped-functional method is constructed to fully utilize inner state information during the whole aperiodic sampling interval. Simultaneously, the positive definiteness constraint for the individual matrix in Lyapunov–Krasovskii functional can be further relaxed. Then, via constructing a novel fuzzy Lyapunov–Krasovskii functional (FLKF) together with a fuzzy-dependent-switching scheme, the available features of fuzzy membership functions (FMFs) can be further taken into consideration to increase the design flexibility. Consequently, the stability condition and corresponding controller design approach for aperiodic sampled-data IT-2 fuzzy systems can be obtained with less design conservatism and larger sampling intervals. Finally, two simulation examples are employed to demonstrate the validity and superiority of the proposed method.
A Wearable Smart Glove and Its Application of Pose and Gesture Detection to Sign Language Classification

Advances in soft sensors coupled with machine learning are enabling increasingly capable wearable systems. Since hand motion in particular can convey useful information for developing intuitive interfaces, glove-based systems can have a significant impact on many application areas. A key remaining challenge for wearables is to capture, process, and analyze data from the high-degree-of-freedom hand in real time. We propose using a commercially available conductive knit to create an unobtrusive network of resistive sensors that spans all hand joints, coupling this with an accelerometer, and deploying machine learning on a low-profile microcontroller to process and classify data. This yields a self-contained wearable device with rich sensing capabilities for hand pose and orientation, low fabrication time, and embedded activity prediction. To demonstrate its capabilities, we use it to detect static poses and dynamic gestures from American Sign Language (ASL). By pre-training a long short-term memory (LSTM) neural network and using tools to deploy it in an embedded context, the glove and an ST microcontroller can classify 12 ASL letters and 12 ASL words in real time. Using a leave-one-experiment-out cross validation methodology, networks successfully classify 96.3% of segmented examples and generate correct rolling predictions during 92.8% of real-time streaming trials.
Leveraging plant physiological dynamics using physical reservoir computing

Plants are complex organisms subject to variable environmental conditions, which influence their physiology and phenotype dynamically. We propose to interpret plants as reservoirs in physical reservoir computing. The physical reservoir computing paradigm originates from computer science; instead of relying on Boolean circuits to perform computations, any substrate that exhibits complex non-linear and temporal dynamics can serve as a computing element. Here, we present the first application of physical reservoir computing with plants. In addition to investigating classical benchmark tasks, we show that Fragaria × ananassa (strawberry) plants can solve environmental and eco-physiological tasks using only eight leaf thickness sensors. Although the results indicate that plants are not suitable for general-purpose computation but are well-suited for eco-physiological tasks such as photosynthetic rate and transpiration rate. Having the means to investigate the information processing by plants improves quantification and understanding of integrative plant responses to dynamic changes in their environment. This first demonstration of physical reservoir computing with plants is key for transitioning towards a holistic view of phenotyping and early stress detection in precision agriculture applications since physical reservoir computing enables us to analyse plant responses in a general way: environmental changes are processed by plants to optimise their phenotype.
Deep Relation Learning for Regression and Its Application to Brain Age Estimation

Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: "cumulative relation," "relative relation," "maximal relation" and "minimal relation." These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).
On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Abstract In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.
A PSO task scheduling and IT2FCM fuzzy data placement strategy for scientific cloud workflows

Scientific workflows are appropriate for treating large volumes of data and leading large-scale experiments. They are time-consuming and resource-intensive applications that profit from operating within distributed platforms, like cloud computing. However, the cloud presents several challenges that must be addressed to employ workflow applications efficiently. Although the workflow scheduling problem has been broadly examined, it still attracts attention. Furthermore, in the literature, little research has been conducted taking into consideration the data placement process in parallel with the execution of task scheduling. Hence, this paper proposes a task scheduling and data placement strategy to reduce the number of data movements between cloud data centers. The suggested strategy uses an algorithm based on the fuzzy clustering method Interval Type-2 Fuzzy C-Means (IT2FCM) and the meta-heuristic optimization technique Particle Swarm Optimization (PSO) to minimize data movements throughout the workflow’s execution. The proposed strategy is evaluated through a simulation process using various well-known scientific workflows of different sizes. Additionally, the outcomes show that our strategy is more reliable than the most recent state-of-the-art techniques.
Lightweight and Effective Deep Image Steganalysis Network

In this letter, a lightweight and effective deep steganalysis network (DSN) with less than 400,000 parameters, called LWENet, is proposed, which focuses on increasing the performance as well as significantly reducing the number of parameters (NP) from three perspectives. Firstly, in the preprocessing part, several lightweight bottleneck residual blocks are combined into the spatial rich model filters to improve the signal-to-noise ratio of stego signals while slightly increasing NP, thereby improving the subsequent performance. Secondly, a depthwise separable convolution layer is exploited at the end of the feature extraction part to largely reduce NP and increase the performance by capturing salient correlations while ignoring trivial ones among feature maps. Finally, to keep LWENet lightweight, we have to select only one fully connected (FC) layer. Simultaneously, multi-view global pooling is employed prior to the FC layer to yield multi-view features and further improve the detection performance. Extensive experiments demonstrate that our network achieves better performance than several state-of-the-art DSNs.
EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks

<h2>Summary</h2> Graph neural networks (GNNs) recursively propagate signals along the edges of an input graph, integrate node feature information with graph structure, and learn object representations. Like other deep neural network models, GNNs have notorious black box character. For GNNs, only few approaches are available to rationalize model decisions. We introduce EdgeSHAPer, a generally applicable method for explaining GNN-based models. The approach is devised to assess edge importance for predictions. Therefore, EdgeSHAPer makes use of the Shapley value concept from game theory. For proof-of-concept, EdgeSHAPer is applied to compound activity prediction, a central task in drug discovery. EdgeSHAPer's edge centricity is relevant for molecular graphs where edges represent chemical bonds. Combined with feature mapping, EdgeSHAPer produces intuitive explanations for compound activity predictions. Compared to a popular node-centric and another edge-centric GNN explanation method, EdgeSHAPer reveals higher resolution in differentiating features determining predictions and identifies minimal pertinent positive feature sets.
An integrated approach to digital training of prospective primary school teachers

The article emphasizes the importance of information and digital technologies in pre-service training of primary school teachers, substantiates the content and components of information and digital competence of prospective primary school teachers. It points out that the main purpose of information and digital training in the pedagogical higher educational institutions (HEI) is to ensure the formation of digital competence of future primary school teachers, to prepare them for developing primary students’ digital literacy in classes on various academic subjects, for active use of ICT in primary school teachers’ professional activities. An integrated approach to the modernization of information and digital training of pre-service primary school teachers, which covers the main forms of the educational process – training sessions, independent work, practical training, and control activities is justified. The article presents the results the pedagogical experiment aimed at testing the effectiveness of the integrated approach to the modernization of information and digital training of prospective primary school teachers. The results are determined by the level of digital literacy and the ability of students in the control and experimental groups to use information and digital technology in the educational process of primary school.