Reviewerly/exHarmony huggingface dataset:
Anomaly Detection using Unsupervised Learning in LTE Mobile Network

Combining the multivariate associated with multiple domains gives the computational edge to solve the complex challenges. We can solve the anomaly detection challenges using the power of the unsupervised learning augmented with the statistical modeling. In this paper, we propose our framework to detect the anomaly cells in a 4G network using the raw counters of 4G mobile network. Our approach is based on modeling the healthy network KPIs in terms of revenue, performance and customer satisfaction using unsupervised learning. We also use the statistical modeling to set the severity of the case to direct the mobile network operator to the health network. This provides flexibility in the operation cost versus the customer perception requirement.
Error Probabilities of Quantum Illumination with Attenuation Using Maximum and Non-maximum Quasi-Bell States

Quantum illumination proposed by Lloyd is a protocol that detects the existence of a target by using entangled light of two-mode. Quantum illumination was first proposed using the Bell state, and then the use of a two-mode Gaussian state was proposed. On the other hand, there are many impact studies using the quasi-Bell state. However, as far as we know, there are no studies that analyze quantum illumination using the quasi-Bell state. Therefore, we focused on the performance evaluation of quantum illumination using the quasi-Bell state in this paper. We compared and considered the effects using the Bell state, the two-mode Gaussian state, the maximum quasi-Bell state, the non-maximum quasi-Bell state, and the coherent state (i.e., a laser radar) on the attenuated channel. In particular, we derived the exact solutions of the error probabilities when using the maximum and non-maximum quasi-Bell states. Then we clarified that quantum illumination using the quasi-Bell state offers a clear advantage over that using the other entangled states under almost situations and always offers a clear advantage over the laser radar.
Digital competence in secondary education teachers during the COVID-19-derived pandemic: comparative analysis

Purpose Continuous training in professional teaching competencies has become one of the challenges of the school of the 21st century, especially if we refer to the mastery of digital competence. The aim of this study is to analyze the degree of digital competence of secondary school teachers of different areas of knowledge during the global pandemic of COVID-19 in the Galician autonomous community and obtain data that allow us to infer whether there is an association between individual variables such as age, gender, degree, work experience and their mastery of digital competence. Design/methodology/approach A cross-sectional descriptive quasi-experimental study is performed using an accidental sample of secondary school teachers from different fields of knowledge. Findings The different results obtained determine that the level of knowledge and use of digital media and tools is low. There are no significant differences depending on the variables analyzed, but it is necessary to establish specific continuing education plans for the improvement of digital competence in secondary school teachers to enable the effective use and management of information and communication technologies in future professionals. Originality/value Therefore, the findings of this study allow the development of educational interventions focused on increasing the digital competence of teachers, taking into account their individual characteristics.
Lion Swarm Optimization Based on Balanced Local and Global Search with Different Distributions

In view of the shortcomings of the basic lion swarm optimization, which is prone to local optimization and low convergence accuracy in partial optimization, this paper proposes a lion swarm optimization based on balanced local and global search with different distributions. The improved algorithm adds chaos search and different distributed perturbation strategies to the positions of lions in the earlier stage, which improves the optimization efficiency of the algorithm in the optimization process. These disturbance strategies include variations based on Cauchy mutation, t probability distribution, and levy flight. The simulation results of the test functions show that the optimization accuracy of the improved algorithm is much higher than that of the basic lion swarm optimization. The improved algorithm effectively prevents the swarm optimization from easily falling into the local optimization value in the extremely difficult optimization functions.
LDVoxelMorph: A precise loss function and cascaded architecture for unsupervised diffeomorphic large displacement registration

The traditional learning-based non-rigid registration methods for medical images are trained by an invariant smoothness regularization parameter, which cannot satisfy the registration accuracy and diffeomorphic property simultaneously. The diffeomorphic property reflects the credibility of the registration results.To improve the diffeomorphic property in 3D medical image registration, we propose a diffeomorphic cascaded network based on the compressed loss (CL), named LDVoxelMorph. The proposed network has several constituent U-Nets and is trained with deep supervision, which uses a different spatial smoothness regularization parameter in each constituent U-Nets for training. This cascade-variant smoothness regularization parameter can maintain the diffeomorphic property in top cascades with large displacement and achieve precise registration in bottom cascades. Besides, we develop the CL as a penalty for the velocity field, which can accurately limit the velocity field that causes the deformation field overlap after the velocity field integration.In our registration experiments, the dice scores of our method were 0.892 ± 0.040 on liver CT datasets SLIVER37 , 0.848 ± 0.044 on liver CT datasets LiTS38 , 0.689 ± 0.014 on brain MRI datasets LPBA38 , and the number of overlapping voxels of deformation field were 325, 159, and 0, respectively. Ablation study shows that the CL improves the diffeomorphic property more effectively than increases.Experiment results show that our method can achieve higher registration accuracy assessed by dice scores and overlapping voxels while maintaining the diffeomorphic property for large deformation.
Masked Batch Normalization to Improve Tracking-Based Sign Language Recognition Using Graph Convolutional Networks

Sign language recognition is a fundamental technique to improve communication between native signers and speakers. Current state-of-the-art sign language recognition methods often apply deep neural network models to learn an optimized projection between sign language videos and sentences in an end-to-end manner. Generally, minibatch training using sequential data requires the addition of padding to equalize the varying lengths of sequences. However, this training strategy induces performance degradation if batch normalization is used in the models because batch normalization assumes the validity of all inputs. In this study, we propose masked batch normalization, which normalizes input features while masking dummy signals. We apply masked batch normalization to tracking-based sign language recognition models using graph convolutional networks. The performance of the proposed method is evaluated in isolated sign language word recognition and continuous sign language words recognition settings. To evaluate the proposed method, we use two types of sign language video datasets, WLASL including 2000 types of isolated words, and a JSL dataset including 275 types of videos of isolated words and 113 types of videos showing sentences. The evaluation results show that the proposed method improves the tracking-based sign language recognition models in both cases.
Impact of artificial intelligence on civilization: Future perspectives

Artificial intelligence is a scientific term that refers to artifacts, detect situations and respond to those circumstances with actions. The ability to create such improved artifacts has more impact on our society. This paper describes the economic and social changes with the use of artificial intelligence since the beginning of smartphones. Smartphones have contributed significantly to big data and that adds more efficiency to machine learning. Artificial intelligence goes on to explain the political, economic, and personal issues that humanity will face soon, as well as regulatory strategies to address them. In general, Artificial intelligence isn't always as precise a generation as one would possibly anticipate, and the problems it increases can be extra vital as a result. Because of extended get entry to understanding of each people and nations, there's a danger of threatening identification and autonomy.
Learning Loss for Active Learning in Depth Reconstruction Problem

Accurate depth estimation from images is a fundamental task in deep learning. It has many applications including scene understanding and reconstruction. Datasets for supervised depth estimation are hard to obtain and usually do not contain a sufficient number of images or a sufficient variety of scenes. Since inputs for depth estimation are simple RGB images, it is easy to obtain a large number of various unlabeled images. We consider that depth masks can be labeled by using manual marking. Thus, we researched the possibility of performing an active learning approach for selecting unlabeled samples to be labeled. In this work, we concentrated on using the learning loss method to perform active learning train selection. We performed multiple experiments with the learning loss algorithm and evaluated the resulting model.
Low-depth optical neural networks

Optical neural network (ONNs) are emerging as attractive proposals for machine-learning applications. However, the stability of ONNs decreases with the circuit depth, limiting the scalability of ONNs for practical uses. Here we demonstrate how to compress the circuit depth to scale only logarithmically in terms of the dimension of the data, leading to an exponential gain in terms of noise robustness. Our low-depth (LD)-ONN is based on an architecture, called Optical CompuTing Of dot-Product UnitS (OCTOPUS), which can also be applied individually as a linear perceptron for solving classification problems. We present both numerical and theoretical evidence showing that LD-ONN can exhibit a significant improvement on robustness, compared with previous ONN proposals based on singular-value decomposition.
Three Birds With One Stone: User Intention Understanding and Influential Neighbor Disclosure for Injection Attack Detection

Recommender system, as a data-driven way to help customers locate products that match their interests, is increasingly critical for providing competitive customer suggestions in many web services. However, recommender systems are highly vulnerable to malicious injection attacks due to their fundamental vulnerabilities and openness. With the endless emergence of new attacks, how to provide a feasible way for defending different malicious threats against online recommendations is still an under-explored issue. In this paper, we explore a new way to defend malicious injection attacks through user intention understanding and influential neighbour disclosure. Specifically, we propose a detection approach, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> hree <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> irds with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> ne <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> tone), to deal with different malicious threats. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> , we first develop the discrimination of attack target by combining global influence evaluation and risk attitude estimation of users. In order to make <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> controllable, second, we propose to incorporate an optimal denoising mechanism to remove disturbed information before detection. To enhance the representativeness and predictability of detection model, finally, we propose to leverage a behavioral label propagation mechanism based on constructed label space for the determination of malicious injection behaviors. Extensive experiments on both synthetic and real data demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> outperforms all baselines in different cases. Particularly, the detection performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> can achieve an improvement of 6.08% FAR (false alarm rate) for optimal-injection attacks, an improvement of 3.83% FAR in average for co-visitation injection attacks, as well as an improvement of 2.3% for profile injection attacks over benchmarks in terms of FAR while keeping the highest DR (detection rate). Additional experiments on real-world data show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> brings an improvement with the advantage of 6.5% FAR in average compared with baselines.
Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

The Phasmatodea population evolution algorithm (PPE) is a novel metaheuristic algorithm proposed in recent years, which simulates the evolutionary trend of stick insect population. In this article, a multigroup-based Phasmatodea population evolution algorithm with mutistrategy (MPPE) is proposed to further improve the overall performance of PPE. During the initialization period, the stick insect population is divided into multiple groups, and the step factor of the flower pollen algorithm is introduced into the population growth model of no more than half of the groups. This makes the population evolution trend diversified and prevents the algorithm from falling into the local optimal solution to a certain extent. In terms of intergroup communication, two communication strategies are adopted to mutate and replace the inferior particles, respectively, which improves the convergence speed and search ability of the algorithm. In the MPPE performance test, we compared it with PPE, five standard algorithms, and other parallel algorithms in CEC 2013 Benchmark Suite. Finally, this algorithm is applied to the IoT based electric bus scheduling for urban waterlogging situation, and the excellent performance of MPPE is verified comprehensively.
Language-Driven Robot Manipulation With Perspective Disambiguation and Placement Optimization

Perspective ambiguity widely exists in human-robot interaction, especially in the language-driven robot manipulation. It is easy to place the object at a wrong position just because of different perspectives. Thus, placement location determination is required in language-driven robot manipulation. In this letter, we introduced a Language-Driven Perspective-based Pick-and-Place Algorithm (LD3PA) to solve this problem. By taking natural language description, image and point cloud from Kinects, a perspective disambiguation network using multi-head self-attention is proposed to infer the user’s perspective. A command analysis network is also designed to decode object’s role and relation from user’s commands. A placement optimization algorithm based on convolution is proposed to find the final placement location and orientation of the picked objects considering the edge of the table and the objects already on the table. Experiments show that our proposed LD3PA could not only correctly disambiguate the user’s perspective, but also find a good location for object placement. The perspective disambiguation ability outperforms human in the comparable experiment. Finally, we implement the algorithm on physical Panda Robot. The result shows that our proposed LD3PA could fuse the multi-modal information to solve the perspective ambiguity and placement optimization problem in human-robot interaction.
Co-Scheduler: A Coflow-Aware Data-Parallel Job Scheduler in Hybrid Electrical/Optical Datacenter Networks

To support higher demand for datacenter networks, networking researchers have proposed hybrid electrical/optical datacenter networks (Hybrid-DCN) that leverages optical circuit switching (OCS) along with traditional electrical packet switching (EPS). However, due to the high <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reconfiguration</i> delay of OCS, OCS is used only for bulk data transfers between racks to amortize the reconfiguration delay. Existing job schedulers for data-parallel frameworks are not designed for Hybrid-DCN, since they neither place tasks to aggregate data traffic to take advantage of OCS, nor schedule tasks to minimize the Coflow completion time (CCT). In this paper, we describe the mismatch between existing job schedulers and the advanced Hybrid-DCN, introduce the requirements for the new scheduler, and present the implementation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co-scheduler</i> , a job scheduler for data-parallel frameworks that aims to improve job performance by placing the tasks of jobs to aggregate enough data traffic to better leverage OCS to minimize the CCT in Hybrid-DCN. Specifically, for every job, Co-scheduler computes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guidelines</i> on how many racks to place the job’s input data and the job’s tasks. The guidelines are dynamically generated based on the real-time job characteristics or predictable job characteristics from prior runs, with the aim of leveraging OCS whenever possible and efficient and minimizing CCT of jobs. Co-scheduler then schedules the tasks of jobs based on the guidelines. We evaluate the effectiveness of Co-scheduler using trace-driven simulation. The evaluation demonstrates that Co-scheduler can improve makespan, average job completion time, and average CCT of a workload by up to 56%, 61%, and 79%, respectively, compared to the state-of-the-art schedulers.
Amharic Adhoc Information Retrieval System Based on Morphological Features

Information retrieval (IR) is one of the most important research and development areas due to the explosion of digital data and the need of accessing relevant information from huge corpora. Although IR systems function well for technologically advanced languages such as English, this is not the case for morphologically complex, under-resourced and less-studied languages such as Amharic. Amharic is a Semitic language characterized by a complex morphology where thousands of words are generated from a single root form through inflection and derivation. This has made the development of Amharic natural language processing (NLP) tools a challenging task. Amharic adhoc retrieval also faces challenges due to scarcity of linguistic resources, tools and standard evaluation corpora. In this research work, we investigate the impact of morphological features on the representation of Amharic documents and queries for adhoc retrieval. We also analyze the effects of stem-based and root-based text representation, and proposed new Amharic IR system architecture. Moreover, we present the resources and corpora we constructed for evaluation of Amharic IR systems and other NLP tools. We conduct various experiments with a TREC-like approach for Amharic IR test collection using a standard evaluation framework and measures. Our findings show that root-based text representation outperforms the conventional stem-based representation on Amharic IR.
Upcycling adversarial attacks for infrared object detection

Recently, infrared object detection (IOD) has been extensively studied due to the rapid growth of deep neural networks (DNNs). An adversarial attack using imperceptible perturbation can dramatically deteriorate the performance of DNNs. Most of the existing adversarial attacks are focused on visible image recognition (VIR), but there are few attacks for IOD. Moreover, the existing attacks are challenging to exploit for state-of-the-art detectors (e.g., EfficientDet) due to low compatibility. To solve the problem, we propose a novel upcycling adversarial attack for IOD by expanding the highly compatible adversarial attacks for the VIR task. We also propose a novel evaluation metric, attack efficiency (AE), to compare the effectiveness of different adversarial attacks. Since the AE value increases with the small perturbation size and the significant performance drop, we can concurrently compare the similarity and performance degradation between adversarial and clean images for various attacks. We validate our approaches through comprehensive experiments on two challenging datasets (e.g., FLIR and MSOD) for the infrared domain.
An Efficient Multi-Model Training Algorithm for Federated Learning

How to effectively organize various heterogeneous clients for effective model training has been a critical issue in federated learning. Existing algorithms in this aspect are all for single model training and are not suitable for parallel multi-model training due to the inefficient utilization of resources at the powerful clients. In this paper, we study the issue of multi-model training in federated learning. The objective is to effectively utilize the heterogeneous resources at clients for parallel multi-model training and therefore maximize the overall training efficiency while ensuring a certain fairness among individual models. For this purpose, we introduce a logarithmic function to characterize the relationship between the model training accuracy and the number of clients involved in the training based on measurement results. We accordingly formulate the multi-model training as an optimization problem to find an assignment to maximize the overall training efficiency while ensuring a log fairness among individual models. We design a Logarithmic Fairness based Multi-model Balancing algorithm (LFMB), which iteratively replaces the already assigned models with a not-assigned model at each client for improving the training efficiency, until no such improvement can be found. Numerical results demonstrate the significantly high performance of LFMB in terms of overall training efficiency and fairness.