Reviewerly/exHarmony huggingface dataset:
Collaborative Guidance of UAV-Transported Semi-Flexible Payloads in Environments with Obstacles

Collaborative load manipulation and transportation is an emerging application of multi-unmanned aerial vehicle (UAV) systems. We address a problem where a team of UAVs transport a semi-flexible payload in an environment with multiple obstacles. By considering a semi-flexible payload, we restrict its flexibility to allow for safe-shape manipulation, which helps to navigate between obstacles while avoiding inter-UAV and payload-obstacle collisions. Control barrier functions (CBFs) are used to construct the obstacle avoidance and shape constraints, which are applied along with actuator constraints to an optimization-based control problem. The analysis is supported with simulation results for a team of four UAVs manipulating a payload in the presence of obstacles and settling on a standoff circle about a target.
ViT - Inception - GAN for Image Colourisation

Studies involving image colourisation have been garnering researchers’ keen attention over time, assisted by significant advances in various Machine Learning techniques and compute power availability. Traditionally, image colourisation has been an intricate task that gave a substantial degree of freedom during the assignment of chromatic information. In our proposed method, we attempt to colourise images using Vision Transformer - Inception - Generative Adversarial Network (ViT-I-GAN), which has an Inception-v3 fusion embedding in the generator. Vision Transformer (ViT) is used as the discriminator for a stable and robust network. The model was trained on the Unsplash and the COCO dataset for demonstrating the improvement made by the Inception-v3 embedding. We have compared the results between ViT-GANs with and without Inception-v3 embedding.
Deformable Geometry based Semantic Reconstruction from Scene Graphs

Structural scene graph based image generation provides a new paradigm for image-oriented semantic communications, whose goal is the semantic level rather than pixel-level reconstruction. The challenges include capturing relationships between objects and producing a reasonable geometric layout for each object accordingly. However, category information alone is not instructive enough for the generation process at the receiver side. Moreover, it is worth effort to extract the spatial dependencies among different objects in an image, therefore determine the object layouts on the whole instead of in an independent manner. In this paper, a deformable geometry framework for scene graph based image generation is proposed, in order to reconstruct images with higher semantic fidelity and visual pleasure. In particular, we introduce shape and appearance information to guide the generation process, from the scope of statistic modeling. Furthermore, we apply a spatial warping network to conduct geometric deformations on the layouts of different objects. Qualitative and quantitative experiments illustrate the superiority of our model compared to the state-of-the-art Sg2im method.
Implementation of Single Camera Markerless Facial Motion Capture using Blendshapes

Facial motion capture is the process of digitizing the facial motion of an actor by locating several facial landmarks of the actor’s face and using the relative coordinates of these landmarks to drive the facial structure of a 3D character in software like Blender. Recent advances have enabled markerless technology to track the desired facial features from frame to frame. In this work, the input from a single front-facing camera is used and the face is located using face detection algorithms. Its output is then used to find the relative coordinates of facial landmarks like lip corners, upper eyelids, and eyebrows etc., using a facial landmark detector. To achieve comparable levels of accuracy without the depth or 3D information that would be captured from a multi-camera setup, morph targets have been used to add constraints to the animation to avoid unnatural positions of the virtual character. The distance between a referential landmark that has minimal movement and the driving landmark determines the influence of the corresponding morph target. To establish an orientation invariant landmark detection, geometric normalization and face size normalization have been deployed.
Client Scheduling for Federated Learning over Wireless Networks: A Submodular Optimization Approach

Federated Learning (FL) has recently been proposed as a distributed optimization framework under resource constraints and privacy concerns. We study the problem of client scheduling for FL, where the goal is to optimize the performance of FL under certain resource constraints on the FL setup. We show that this problem is a special instance of the general problem of maximizing a submodular function subject to a submodular upper bound constraint. We propose a greedy algorithm to solve this general problem, and provide theoretical approximation guarantees to characterize its performance. The greedy algorithm proposed for the general problem is then applied to solve the FL client scheduling problem with the approximation guarantee. We evaluate the performance of the algorithm using experiments.
Still room for improvement in traditional 3D interaction: selecting the fixed axis in the virtual trackball

Abstract Virtual trackball techniques are widely used when 3D interaction is performed through interfaces with a reduced number of degrees of freedom such as mice and touchscreens. For decades, most implementations fix a vertical axis of rotation, which is a suitable choice when the vertical axis should indeed be fixed, according to some mental model of the user. We conducted an experiment involving the use of a mouse and a touch device to study usability in terms of performance, perceived usability and mental workload when selecting different fixed axes in accordance with the user’s mental model. The results we obtained indicate that the consistency between the axis fixed by the technique and the object’s intrinsic axis has a positive effect on usability. We believe that implementations that allow to select different fixed axis for each specific object should be considered when designing future reduced-DoF interaction interfaces.
Classifying Near-Miss Traffic Incidents through Video, Sensor, and Object Features

Front video and sensor data captured by vehicle-mounted event recorders are used for not only traffic accident evidence but also safe-driving education as near-miss traffic incident data. However, most event recorder (ER) data shows only regular driving events. To utilize near-miss data for safe-driving education, we need to be able to easily and rapidly locate the appropriate data from large amounts of ER data through labels attached to the scenes/events of interest. This paper proposes a method that can automatically identify near-misses with objects such as pedestrians and bicycles by processing the ER data. The proposed method extracts two deep feature representations that consider car status and the environment surrounding the car. The first feature representation is generated by considering the temporal transitions of car status. The second one can extract the positional relationship between the car and surrounding objects by processing object detection results. Experiments on actual ER data demonstrate that the proposed method can accurately identify and tag near-miss events.
Study of Variation of Plant Types of Vertical SubSurface Constructed Wetland Treating Groundwater Contaminated with Iron (Fe)

Drilling groundwater at the BSD Housing Complex, Malintang Village, Gambut District, Banjar Regency has an iron (Fe) value of 5.98 mg/L. According to the Minister of Health Number 32 of 2017 concerning water health requirements for hygiene and sanitation purposes, the water is above the quality standard, 1.0 mg/L for Iron (Fe). One of the treatment methods used to treat the contaminated groundwater is constructed wetland (CW). CW was chosen because of its relatively low cost and environmentally sound natural wetlands. We used the Vertical Flow Subsurface Constructed Wetland (VSSF-CW) in this study. This study aims to determine the effectiveness of the VSSF-CW system with different local plants, namely Jeringau (Acorus calamus) and Purun tikus (Eleocharis dulcis), in removing iron (Fe) in groundwater. The study used five wood reactors with 90 cm x 30 cm x 40 cm. The results showed that the VSSF-CW system with E. dulcis plant effectively removed iron (Fe) with a higher efficiency value of 85.68% compared to the reactor with A. calamus plant that was 76.72%, and reactor without plant 59,37%. The lowest efficiency of each reactor during the study was on day 5, with values of 58.28%, 37.88%, and 29.43%, respectively. The concentration of iron (Fe) in the VSSF-CW system effluent with Acorus calamus and Eleocharis dulcis plants for 40 days has a value that tends to decrease. Keywords: Vertical Flow Subsurface Constructed Wetland, groundwater, iron, Acorus calamus, Eleocharis dulcis.
Incentive and Game of Information Sharing Based on Blockchain Technology

With the development of the information age, the efficiency of information resource sharing has not been fully released, and the willingness of participants to share information needs to be improved. It is of great significance to promote the willingness of participants to share information and increase the value of information utilization. Aiming at the problem of low information sharing rate of participants, this paper constructs an evolutionary game model between information sharing and non-sharing participants under the blockchain technology platform and introduces the critical value corresponding to the initial proportion of participants under the condition of a stable strategy as a trigger Conditions, dynamic adjustment of incentive coefficients to maintain a stable strategy of information sharing, simulation with MATLAB. Research shows that the use of blockchain smart contract technology to dynamically adjust the evolutionary stability strategy is shown as: When the initial proportion of participants is lower than the critical value, the incentive parameters are transformed into shared incentive benefits to motivate the participants to actively participate in information sharing; when it is higher than the critical value, the incentive parameters are transformed into sharing costs and dynamically adjusted to reach the initial participants. The proportion is maximized, which provides a strategy for participants to choose information-sharing strategies.
SimpleZSL: Extremely Simple and Fast Zero-Shot Learning with Nearest Neighbor Classifiers

Zero-shot learning (ZSL) aims to classify images of unseen classes by learning relationship between visual and semantic features. Existing works have been improving recognition accuracy from various approaches, but they employ computationally intensive algorithms that require iterative optimization. In this work, we revisit the primary approach of the pattern recognition, ı.e., nearest neighbor classifiers, to solve the ZSL task by an extremely simple and fast way, called SimpleZSL. Our algorithm consists of the following three simple techniques: (1) just averaging feature vectors to obtain visual prototypes of seen classes, (2) calculating a pseudo-inverse matrix via singular value decomposition to generate visual features of unseen classes, and (3) inferring unseen classes by a nearest neighbor classifier in which cosine similarity is used to measure distance between feature vectors. Through the experiments on common datasets, the proposed method achieves good recognition accuracy with drastically small computational costs. The execution time of the proposed method on a single CPU is more than 100 times faster than those of the GPU implementations of the existing methods with comparable accuracies.
Research on Security Protection Method of Industrial Control Boundary Network

Aiming at the problems of single protection, lack of monitoring and unable to be physically isolated in time under abnormal conditions, an industrial control boundary network security protection method is provided. Realize the real-time monitoring and analysis of the network behavior of the industrial control boundary, realize the in-depth defense of the industrial control boundary, and timely block it in the way of logical link and physical link isolation in case of illegal intrusion, so as to comprehensively improve the protection level of the boundary security of the industrial control system.
Network Embedding for Cluster Analysis

Graph visualization is an effective and efficient way to discover complex inter-connections between elements within the nested structure of data. To accomplish this type of representation machine learning algorithms use a technique called graph embedding and node embedding in particular. However, in this paper, we will compare well-known techniques to yet largely under-explored setting of graph embedding named community embedding: embedding individual communities instead of individual nodes. This type of embedding can be especially useful in graph visualization and community detection tasks. Despite the fact that graph embedding and clustering tasks are separate, a good solution to the first one tends to have a correlation with the solution of the second problem and may have a positive impact if knowledge is transferred.
Expression Recognition Across Age

Expression recognition is an important and growing field in AI. It has applications in fields including, but not limited to, medicine, security, and entertainment. A large portion of research, in this area, has focused on recognizing expressions of young and middle-age adults with less focus on children and elderly subjects. This focus can lead to unintentional bias across age, resulting in less accurate models. Considering this, we investigate the impact of age on expression recognition. To facilitate this investigation, we evaluate two state-of-the-art datasets, that focus on different age ranges (children and elderly), namely EmoReact and ElderReact. We propose a Siamese-network-based approach that learns the semantic similarity of expressions relative to each age. We show that the proposed approach, to expression recognition, is able to generalize across age. We show the proposed approach is comparable to or outperforms current state-of-the-art on the EmoReact and ElderReact datasets.
Symmetry-Induced Emergence of a Pseudo-Qutrit in the Dipolar Coupling of Two Qubits

We investigate a system of two identical and distinguishable spins 1/2, with a direct magnetic dipole-dipole interaction, in an external magnetic field. Constraining the hyperfine tensor to exhibit axial symmetry generates the notable symmetry properties of the corresponding Hamiltonian model. In fact, we show that the reduction of the anisotropy induces the invariance of the Hamiltonian in the 3×3 subspace of the Hilbert space of the two spins in which S^2 invariably assumes its highest eigenvalue of 2. By means of appropriate mapping, it is then possible to choose initial density matrices of the two-spin system that evolve in such a way as to exactly simulate the time evolution of a pseudo-qutrit, in the sense that the the actual two-spin system nests the subdynamics of a qutrit regardless of the strength of the magnetic field. The occurrence of this dynamic similitude is investigated using two types of representation for the initial density matrix of the two spins. We show that the qutrit state emerges when the initial polarizations and probability vectors of the two spins are equal to each other. Further restrictions on the components of the probability vectors are reported and discussed.
SimGQ+: Simultaneously evaluating iterative point-to-all and point-to-point graph queries

Graph processing frameworks are typically designed to optimize the evaluation of a single graph query. However, in practice, we often need to respond to multiple graph queries, either from different users or from a single user performing a complex analytics task. Therefore in this paper we develop SimGQ+, a system that optimizes simultaneous evaluation of a group of vertex queries that originate at different source vertices (e.g., multiple shortest path queries originating at different source vertices) and delivers substantial speedups over a conventional framework that evaluates and responds to queries one by one. Our work considers both point-to-all and point-to-point queries. The performance benefits are achieved via batching and sharing. Batching fully utilizes system resources to evaluate a batch of queries and amortizes runtime overheads incurred due to fetching vertices and edge lists, synchronizing threads, and maintaining computation frontiers. Sharing dynamically identifies shared queries that substantially represent subcomputations in the evaluation of different queries in a batch, evaluates the shared queries, and then uses their results to accelerate the evaluation of all queries in the batch. With four input power-law graphs and four graph algorithms SimGQ+ achieves speedups of up to 45.67× with batch sizes of up to 512 queries over the baseline implementation that evaluates the queries one by one using the state of the art Ligra system. Moreover, both batching and sharing contribute substantially to the speedups.
A statistical feature data mining framework for constructing scholars’ career trajectories in academic data

Temporal and spatial information about scholars can be found in their academic papers. Examining the footprints of scholars’ careers can help us understand the course of their growth, potential collaborations for future research, and trends in the flow of talent. Although a great deal of research has been conducted in related fields, the challenge of accurately constructing scholars’ career trajectories from redundant and noisy academic data is far from resolved. To address this problem, a unified framework called ATrajRN that employs AMiner academic data is proposed for the first time. To accurately obtain scholars’ geographic location information from their research achievements, this study introduces an algorithm called Positioning based on Academic Achievements of Scholars (PAAS), which aims to make the most of academic data and the characteristics of different maps. To avoid the interference of data redundancy, this paper proposes a statistical feature-based method to find the most reliable career trajectories by some state-of-the-art approaches. To restore the continuously scholars’ career trajectories, this paper offers the trajectory generation algorithm based on the output from the previous step. Experiments and systematic analysis shows that the proposed novel method could achieve approximately 80% accuracy – an increase of approximately 10% – manifestly outperform the baseline method. Lastly, based on this work, we develop a system for understanding scholars’ trajectories through analysis and visualization, and we investigate the migration characteristics of typical scholar groups.
Integrating Automated Biomedical Lexicon Creation for Valley Fever Diagnosis

Named Entity Recognition (NER) is an important task in biomedical NLP which identifies and categorizes entities in biomedical text. We currently focus on a rule-based approach for NER to identify the diagnostic criteria of valley fever in the free text of electronic health records (EHRs), since no training data exist for machine learning. To aid the manual pattern defining process of the rule-based approach, we propose a graph-based lexicon expansion method. We used different word embedding models to create a lexicon graph and expanded the lexicons by conducting different graph search methods.
Depthwise Over-parameterized Siamese Network for Visual Tracking

Siamese Network based trackers have achieved excellent performance because of their balanced accuracy and speed. Extracting effective feature of the target template and search regions is a very important problem in visual tracking. Generally, existing trackers use Convolutional Neural Network (CNN) to extract features, which make full use of the depth feature while the spatial structure information is ignored. The spatial structure information is very helpful to represent appearance characteristics of targets and templates. In this paper, we propose a novel tracking algorithm based on Siamese network. Specifically, it consists of Siamese subnetwork for feature extraction and cross correlation. Subnetwork improves feature learning capability of extracting shallow spatial information and deep semantic information, and accelerates the model training. Extensive experiments conducted on three benchmarks including OTB2015 and VOT2016 show that the proposed DOSiam tracker has superior performances and real-time response against state-of-the-art trackers while runs at more than 60 FPS.
Encryption and Decryption Cloud Computing Data Based on XOR and Genetic Algorithm

Cloud computing is a very large storage space, can be accessed via an internet connection, this concept has appeared to facilitate the preservation of personal and corporate data and the easily of sharing, and this data can also be accessed from anywhere in the world as long as it is on the Internet, large gaps have emerged around data theft and viewing. Accordingly, researchers have developed algorithms and methods to protect this data, but the attempts to penetrate the data did not stop. In this research, we developed a method that combines XOR and Genetic algorithm to protect the data on the cloud through encryption operations and keep the key from being lost or stolen. The data that is uploaded to cloud computing may be important and we should not allow any party to see it or steal it. Therefore, it became imperative to protect this data and encrypt it. We have developed an algorithm that uses XOR and genetic algorithms in the encryption process.
Internet Addiction

Different educational programs aim to reduce or control Internet addiction. These are based on a traditional methodology that can be recognized as an exposition or master class (participatory or not). In this type of educational programs the student acquires a passive role since they are focused on informing the students of the dangers associated with the development of Internet addiction, but not on the treatment or changes in behavior. If we review these educational programs it can see how they have little effect on students. For this reason, a new educational program approach has been created that fights or limits Internet addiction through a methodological proposal that also focuses on increasing digital competence while limiting or controlling the evolution of Internet addiction. In this article, the design is presented.
On Evaluating Delegated Digital Signing of Broadcasting Messages in 5G

In 5G networks, base stations, namely gNBs (5G NodeB, as per 3GPP nomenclature) periodically broadcast the system information messages including network identifiers to facilitate User Equipment (UE) to connect to the network. As in prior generations, the system information messages in 5G are transmitted in clear text without any security protection. Therefore, an adversary could spoof a legitimate gNB to become a man-on-the-side (MOTS) or man-in-the-middle (MITM) attacker. This vulnerability is being studied by 3GPP and a number of solutions have been proposed in the Technical Report (TR 33.809), including a promising solution namely Digital Signing Network Function (DSnF). In this paper, we provided an evaluation of DSnF, including the practicality of its assumption, feasibility of its certificate trans-mission within the system information message, and quantitative analysis of its performance. Our evaluation results show that DSnF is practical in general. Initial results from this paper have been provided to 3GPP and incorporated into TR 33.809.