Reviewerly/exHarmony huggingface dataset:
Docker Container Networking Based Apache Storm and Flink Benchmark Test

Many distributed stream computing engines have emerged to handle big data, and they can be deployed in cloud environments consisting of native networks or container networks. Most of the benchmark research on stream computing engines are carried out under the native network, and the research on the impact on container network on stream computing engines is currently inadequate. However, the use of container network will inevitably lead to performance degradation, which is the disadvantage of all virtual networks. In this work, we build Apache Storm and Apache Flink, which are Streaming Computation Engines in container network and native network environments and conduct performance measurements through experiments processing textual data to verify how much performance decreases in container network. Experiments show that the throughput in a container network environment is 1%-5% lower and CPU utilization is 11%-18% lower than in a local network environment.
Fractional revival on non-cospectral vertices

Perfect state transfer and fractional revival can be used to move information between pairs of vertices in a quantum network. While perfect state transfer has received a lot of attention, fractional revival is newer and less studied. One problem is to determine the differences between perfect state transfer and fractional revival. If perfect state transfer occurs between two vertices in a graph, the vertices must be cospectral. Further if there is perfect state transfer between vertices a and b in a graph, there cannot be perfect state transfer from a to any other vertex. No examples of unweighted graphs with fractional revival between non-cospectral vertices were known; here we give an infinite family of such graphs. No examples of unweighted graphs where the pairs involved in fractional revival overlapped were known; we give examples of such graphs as well.
Encrypted Network Traffic Identification Based on 2D-CNN Model

Rapid development of the Internet has enabled explosive growth of various network traffic. How to classify and identify different categories of network traffic among these huge network traffic for cyberspace security has always been a hot research topic. In our study, we found that the composition structure of data frames and grayscale maps in the original traffic is very similar. Combined with recent research of deep learning in image processing, this paper proposes a 2D-CNN model-based network traffic recognition algorithm, while transforming traffic to grayscale maps for recognition. To validate the effectiveness of our proposed model, we use the public network dataset ISCX-VPN-NonVPN-2016 and USTC-TF2016. Experimental results prove that the average accuracy is 98.7% in regular encrypted traffic identification and 97.6% for malicious traffic identification. Our method provides new solutions for network traffic identification.
Learning Optical Flow with R-CNN for Visual Odometry

Addressing on monocular visual odometry problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent space of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF latent space is used to compute the regression of the 6-dimensional pose vector. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our model outperforms most learning-based VO approaches.
Relationship Between Defects of Capacitive Equipment and Geomorphology

In the power system, there are a large number of capacitive devices, accounting for 40% to 50% of the total substation equipment. The healthy operation of capacitive devices is vital to the power system. With the vigorous advancement of power grid information construction, various types of power data have exploded, which has provided strong data support for the healthy operation of capacitive equipment. There is little research on the relationship between the defects of capacitive equipment and topography at home and abroad, such as altitude, slope, and aspect. This project conducts research on the relationship between defects of capacitive equipment and topography, and provides technical support and assistance for decision-making of power system construction. Firstly, 60 TIFF images with a resolution of 30m were obtained from the website, and 60 images were stitched using ArcGIS software, and then the slope and aspect data of the target location were extracted based on the latitude and longitude information. Secondly, we perform data cleaning and code on the original data. Thirdly, the relationship between the number of occurrences of equipment defects and altitude, slope, and aspect is programmed. The correlation is roughly deduced by calculating the number of occurrences of equipment defects and the parameters such as the covariance, correlation coefficient.
A Connected Component Labelling algorithm for a multi-pixel per clock cycle video stream

This work describes the hardware implementation of a connected component labelling (CCL) module in reprogammable logic. The main novelty of the design is the "full", i.e. without any simplifications, support of a 4 pixel per clock format (4 ppc) and real-time processing of a 4K/UltraHD video stream (3840 x 2160 pixels) at 60 frames per second. To achieve this, a special labelling method was designed and a functionality that stops the input data stream in order to process pixel groups which require writing more than one merger into the equivalence table. The proposed module was verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the ZCU104 evaluation board.
The Effectiveness of Virtual Reality Technology Applying in Vocational Training on Occupational Competency among the Students with Intellectual Disability in Senior High School

Under the atmosphere of social inclusion, it becomes the essential goal of trans-disciplines professionals to promote people with intellectual disability (ID) work in community independently. Nevertheless, limitation of cognitive function of people with ID results in their difficulties in learning and insufficient vocational ability. So our team will develop the Virtual reality technology (VRT) system which will allow ID students to learn and adapt to the complex workplace effectively.
Evaluating attractiveness of cyberattack path using resistance concept and page-rank algorithm

The industrial control systems (ICSs) and instrumentation and control (I&C) systems of nuclear power plants (NPPs) have changed from analog systems to digital systems. Digital systems offer maintenance and usability advantages, but are prone to cyberattacks. The conventional belief that digital systems are essentially free from cyberattacks owing to the presence of air gaps has been negated by several attacks on actual NPPs. Theoretically, a system can be secured by using all possible security measures. However, in reality, the resources available for cybersecurity are limited. Therefore, the efficient distribution of resources is a critical issue. The conventional methods were mainly focused on impairment failure. In this study, we propose a system that suggests the most attractive attack path that can consider both type 1 (impairment failure) and type 2 (Byzantine failure) security failure. The proposed algorithm consists of two parts: a graph map that evaluates the attractiveness of an attack path, and a path-finding part that searches for the shortest path in the graph map. To quantify the attractiveness of the attack path, we propose a resistance concept. In addition, we quantify the importance of components in the system using a page-rank algorithm. We adopt Dijkstra's algorithm to automatically determine the most attractive path. With the proposed methodology, it is expected that an attractive attack path that considers both type 1 and type 2 security failure can be found, and a corresponding efficient cybersecurity strategy can be established.
Learning from Demonstration without Demonstrations

State-of-the-art reinforcement learning (RL) algorithms suffer from high sample complexity, particularly in the sparse reward case. A popular strategy for mitigating this problem is to learn control policies by imitating a set of expert demonstrations. The drawback of such approaches is that an expert needs to produce demonstrations, which may be costly in practice. To address this shortcoming, we propose Probabilistic Planning for Demonstration Discovery (P2D2), a technique for automatically discovering demonstrations without access to an expert. We formulate discovering demonstrations as a search problem and leverage widely-used planning algorithms such as Rapidly-exploring Random Tree to find demonstration trajectories. These demonstrations are used to initialize a policy, then refined by a generic RL algorithm. We provide theoretical guarantees of P2D2 finding successful trajectories, as well as bounds for its sampling complexity. We experimentally demonstrate the method outperforms classic and intrinsic exploration RL techniques in a range of classic control and robotics tasks, requiring only a fraction of exploration samples and achieving better asymptotic performance.
Construction of irreducible polynomials over finite fields

Irreducible polynomials over finite fields and their applications have been quite well studied. Here, we discuss the construction of the irreducible polynomials of degree [Formula: see text] over the finite field [Formula: see text] for a given irreducible polynomial of degree [Formula: see text]. Furthermore, we construct the irreducible polynomials of degree [Formula: see text] over the finite field [Formula: see text] for a given irreducible polynomial of degree [Formula: see text] by using the method of composition of polynomials with some conditions on coefficients and degree of a given irreducible polynomial.
Improving of Imbalanced Data in Multiclass Classification for Sentiment Analysis using Supervised Term Weighting

Sentiment classification (SC) is an ongoing field of research, which involves computing opinions, sentiments, and the subjectivity of a text. It has recently been proven that imbalanced classification is challenging for the SC research community. Most existing studies assume that the balance between negative and positive samples may not be true in reality. This work describes a method to improve the problem of imbalanced sentiment classification using supervised term weighting schemes and shows how these weighting schemes can improve the performance of sentiment classification with imbalanced data, especially in the domain of multi-class classification. Nonetheless, to obtain the most appropriate term weighting schemes, five term weighting schemes are comparatively studied, namely tf-idf, tf-idf-icf, tf-rf, tf-igm, and sqrt_tf-igm. In addition to comparing several term weightings schemes, this work also compares four supervised machine learning algorithms to obtain an appropriate algorithm, including k-Nearest Neighbor (k-NN), Multinomial Naïve Bayes (MNB), Support Vector Machines (SVM) with linear, and SVM with RBF. After evaluating by F1, the performance of sqrt_tf-igm was superior to all other weighting schemes. Since the overall picture of sqrt_tf-igm returned better results than the tf-idf, tf-idf-icf, and tf-rf methods, with improved scores of F1 at 10.94%. Meanwhile, the result of sqrt_tf-igm was slightly better than tf-igm.
Invertable Frowns

We present Wav2Lip-Emotion, a video-to-video translation architecture that modifies facial expressions of emotion in videos of speakers. Previous work modifies emotion in images, uses a single image to produce a video with animated emotion, or puppets facial expressions in videos with landmarks from a reference video. However, many use cases such as modifying an actor's performance in post-production, coaching individuals to be more animated speakers, or touching up emotion in a teleconference require a video-to-video translation approach. We explore a method to maintain speakers' identity and pose while translating their expressed emotion. Our approach extends an existing multi-modal lip synchronization architecture to modify the speaker's emotion using L1 reconstruction and pre-trained emotion objectives. We also propose a novel automated emotion evaluation approach and corroborate it with a user study. These find that we succeed in modifying emotion while maintaining lip synchronization. Visual quality is somewhat diminished, with a trade off between greater emotion modification and visual quality between model variants. Nevertheless, we demonstrate (1) that facial expressions of emotion can be modified with nothing other than L1 reconstruction and pre-trained emotion objectives and (2) that our automated emotion evaluation approach aligns with human judgements.
An Empirical Investigation of PU Learning for Predicting Length of Stay

Reliably predicting the length of stay of patients in a hospital based on their demographic and clinical characteristics as well as the care they received can inform hospital planning, particularly in novel response scenarios such as Covid-19. Positive Unlabelled (PU) learning is a type of semi-supervised learning in which only the positive labels in a dataset are reliable. PU learning can be used when the length of stay prediction is formulated as a classification problem, and the prediction needs to be performed dynamically while the patients are being treated. This paper empirically investigates how unlabeling can negatively affect classification accuracy and show how this effect can be mitigated using different algorithms for PU learning. A large dataset of Covid-19 length of hospital stay was used for the experiments. The results show the potential of utilizing PU learning approaches to predicting the length of hospital stay.
An Empirical Study of Inter-cluster Resource Orchestration within Federated Cloud Clusters

Federated clusters are composed of multiple independent clusters of machines interconnected by a resource management system, and possess several advantages over centralized cloud datacenter clusters including seamless provisioning of applications across large geographic regions, greater fault tolerance, and increased cluster resource utilization. However, while existing resource management systems for federated clusters are capable of improving application intra-cluster performance, they do not capture inter-cluster performance in their decision making. This is important given federated clusters must execute a wide variety of applications possessing heterogeneous system architectures, which are a impacted by unique inter-cluster performance conditions such as network latency and localized cluster resource contention. In this work we present an empirical study demonstrating how inter-cluster performance conditions negatively impact federated cluster orchestration systems. We conduct a series of micro-benchmarks under various cluster operational scenarios showing the critical importance in capturing inter-cluster performance for resource orchestration in federated clusters. From this benchmark, we determine precise limitations in existing federated orchestration, and highlight key insights to design future orchestration systems. Findings of notable interest entail different application types exhibiting innate performance affinities across various federated cluster operational conditions, and experience substantial performance degradation from even minor increases to latency (8.7x) and resource contention (12.0x) in comparison to centralized cluster architectures.
Saliency-Guided Complementary Attention for Improved Few-Shot Learning

Despite significant progress in recent deep neural networks, most deep learning algorithms rely heavily on abundant training samples. To address this problem, we propose an effective and interpretable few-shot classification model using Saliency-Guided Complementary Attention (SGCA), which aims to learn transferable representations and to build a robust classification module simultaneously. Concretely, we propose to train our feature extractor using an auxiliary task to separate object regions from background clutter guided by saliency detection signals. In addition, to make the separation beneficial to the downstream tasks, we introduce a complementary attention mechanism to force the classification module to focus on various informative parts of the image. Extensive experiments on few-shot learning tasks demonstrate the effectiveness of our proposed method, e.g., we achieve 68.81% and 84.60% for 5-way 1-shot and 5-shot settings on mini-ImageNet, respectively.
Explaining Black-box Predictions by Generating Local Meaningful Perturbations

Generating explanations of predictions made by machine learning models is a difficult task, especially for black-box models. One possible way to explain an individual decision or recommendation for a given instance is to build an interpretable local surrogate for the underlying black-box model in the vicinity of the given instance. This approach has been adopted by many algorithms, for example LIME and LEAFAGE. These algorithms suffer from shortcomings, strict assumptions and prerequisites, which not only limit their applicability but also affect black-box fidelity of their local approximations. We present ways to overcome their shortcomings including the definition of neighborhood, removal of prerequisites and assumption of linearity in local model. The main contribution of this paper is a novel algorithm (LEMP) which provides explanation for the given instance by building a surrogate model using generated perturbations in the neighborhood of the given instance as training data. Experiments show that our approach is more widely applicable and generates interpretable models with better fidelity to the underlying black-box model than previous algorithms.
β-Hill Climbing Grey Wolf Optimizer

The grey wolf optimizer (GWO) is a promising neoteric algorithm for the optimization of swarm intellect and it draws inspiration from the power structure of Canis lupus wolves. There may be some downsides to the current grey wolf optimization algorithm, like slow convergence speed, low precision, and so forth. Here, a neoteric hybrid algorithm focused on grey wolf optimizer (GWO) and β-hill climbing algorithm called β-hill climbing grey wolf optimizer (β-HCGWO) is suggested to gain advantages of the latter one and overcome the disadvantages of GWO and help reach global optima effectively. The prime reason causing the fusion of GWO with β-hill climbing is to help the algorithm avoid premature convergence and form a balance between exploration and exploitation. The proposed hybrid algorithm was assessed and looked at utilizing 11 standard benchmark functions. The empirical calculations recommend that the experimental calculations of the introduced strategy deliver progressively precise outcomes in a briefer time period contrasted with the primitive GWO and different frameworks.
GRETA//A PLASTIC POEM: An Integrated Approach to the Vibrant Matter of Voice, Deep Listening, and Somatic Movement in Sonic Performance Art as Plastic Activism

Through an analysis of the sonic performance artwork GRETA//A PLASTIC POEM (2020), this paper analyzes why and when participatory art practices can intra-sect and converge with ecologies to imagine posthuman futures and challenge pre-existing structures of thought. From an activist position that considers art and the environment as a participatory collective model, this article argues for a reappraisal of human influence in complex human–non-human interactional systems. More specifically, this text, as a component of GRETA//A PLASTIC POEM, approaches oceanic and sea pollution (notably the dumping of plastics) as a form of environmental degradation that has generated a space inhabited by varied and vibrant forms of matter. In this way, the sea and the sonic space show strikingly similar potentials. In turn, Plastic Activism, which advocates for a world free of plastic pollution and its toxic impacts, is adopted as an underlying ethos and aesthetic response, with single-use plastics being incorporated into the performance artwork itself. Relational to this enquiry is sensing how we use our bodies to explore the potential thousands of distinct material ecologies embodying a sonic performative practice, especially combining the materiality of artistic and political action in public space and focal practices. In this way, the sonic space becomes comparable to a sea of sound, where complex assemblages of matter exist and intra-act to challenge the discordant systems and dynamics in which we are currently entangled. Article received: April 25, 2021; Article accepted: June 21, 2021; Published online: September 15, 2021; Original scholarly paper