Reviewerly/exHarmony huggingface dataset:
Viewer-Oriented CDN Scheduling on Crowdsourced Live Video Stream

In crowdsourced live video stream platforms, viewers are spread all over the world. To improve the Quality of Experience (QoE), streams are distributed to the content distribution network (CDN) deployed in different regions, and viewers are assigned to the closest CDN to watch live broadcast. Currently, in these platforms, like Douyu and Twitch, the stream distribution strategy is based on the popularity of stream, without considering viewers' engagement, which is significant to the platform. In this paper, taking Douyu as a case, we conduct a detailed analysis of the platform. Our results show the gap between popularity based stream distribution strategy and platform's profit. To fill the gap, we propose a viewer-oriented algorithm to distribute stream and determine viewer's streaming source based on viewer's engagement, which targets maximize platform's profit. We examine our algorithm using real trace of Douyu. The results indicate that our algorithm can improve the overall profit of platform and uniformity of load in CDN with low scheduling cost.
Initial Report: A Novel Intraoperative Navigation System for Laparoscopic Liver Resection Using Real-Time Virtual Sonography

Abstract Recent progress in navigation has revealed problems involving non-rigid registration for hepatic surgery. With the increasing popularity of laparoscopic liver surgery, a new laparoscopic navigation system is necessary. This study involved an in-vitro demonstration of a 3-dimensional printer model and in vivo demonstration in four patients. For the in vitro examination, a position detecting unit attached at 33 cm and 13 cm distance conditions from the tip of the electrocautery was examined eight times at the marked points on the liver surface eight times respectively. The differences between the simulation and the authentic dissecting plane were conventionally investigated in vivo . In vitro , the errors of the 33 cm and 13 cm distance model were7.8 ± 3.5 mm (mean ± SD), and 3.3 ± 1.0 mm, respectively. The mean differences of the dissection plane were within 10 mm. The potentiality and safety of the novel navigation system was confirmed, although further investigation is recommended.
Multi-Scale Residual Network for Image Classification

Multi-scale approach representing image objects at various levels-of-details has been applied to various computer vision tasks. Existing image classification approaches place more emphasis on multi-scale convolution kernels, and overlook multi-scale feature maps. As such, some shallower information of the network will not be fully utilized. In this paper, we propose the Multi-Scale Residual (MSR) module that integrates multi-scale feature maps of the underlying information to the last layer of Convolutional Neural Network. Our proposed method significantly enhances the characteristics of the information in the final classification. Extensive experiments conducted on CIFAR100, Tiny-ImageNet and large-scale CalTech-256 datasets demonstrate the effectiveness of our method compared with Res-Family.
Stabilizing Multi-Agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors

Deep reinforcement learning (DRL) is able to learn control policies for many complicated tasks, but it's power has not been unleashed to handle multi-agent circumstances. Independent learning, where each agent treats others as part of the environment and learns its own policy without considering others' policies is a simple way to apply DRL to multi-agent tasks. However, since agents' policies change as learning proceeds, from the perspective of each agent, the environment is non-stationary, which makes conventional DRL methods inefficient. To cope with this challenge, we propose a novel approach where each agent uses an implicit estimate of others' actions to guide its own policy learning. We demonstrate that given the implicit estimate of others' actions, each agent can learn its policy in a relatively stationary environment. Extensive experiments show that our method significantly alleviates the non-stationarity and outperforms the state-of-the-art in terms of both convergence speed and policy performance.
Usability Matters: A Human–Computer Interaction Study on Network Management Tools

Over the years, network monitoring tools have evolved to keep pace with the advances in networking technologies and paradigms, such as virtualization and softwarization, and more recently, data plane programmability. However, there have been few advances regarding user interfaces and interactions in network management systems. In this paper, an investigation based on the human-computer interaction perspective is presented and improvements on the usability aspects of network monitoring tools are proposed. First, we conducted a survey to capture general information from network administrator professionals. On the basis of these initial data, we identified and specified two personas to represent the groups of users. Thereafter, a study focused on observing users' interaction and experience with a well-known network monitoring tool was conducted. Finally, our qualitative analysis revealed several findings that were used to develop recommendations to aid developers in the (re)design of these tools to enhance end-user interaction.
Geochemistry features of sediments of small urban arctic Lake Komsomolskoye, Murmansk region

Abstract First data of geochemistry and mineralogy of recent sediments of small Lake Komsomolskoye located in Monchegorsk town from Murmansk region (Russia) are presented. The high concentrations of heavy metals were found in surface layers of researched lake sediments. The most accumulation level is initialled for copper and nickel, which are entered to lake from copper-nickel plant complex situated near the arctic town. Other metals also can enter to waterbody in result of activity of this factory complex. In addition, lead, cadmium, antimony, thallium and other elements may be products of the atmospheric transport of pollutants from industries of North and East Europe. Moreover, we have studied main fractions of heavy metals from upper layers of Lake Komsomolskoye and morphology and composition of technogenic particles of nickel, copper, iron and other metals. The origin of these particles is directly associated with the different stages of processing technology of nickel and copper ore on the copper-nickel plant complex (crushing, flotation, roasting, and smelting).
A SMART Goal-based Framework for Privacy Preserving Embedded Forensic Investigations

Forensic investigations obtain as much information from a crime suspect to gather evidence for the case. However, such investigations performed on embedded devices may invade the privacy of those possessing the devices. An embedded device is a valuable potential source of evidence and also is a personal device consisting of private data. In our research, we discuss the various implications of data privacy laws on embedded device forensics investigations, the role of anti-forensics and propose set of policies and measures for handling the privacy concerns to embedded forensic investigations. We also propose a SMART framework for enabling privacy-preserving embedded digital forensic investigations at every phase of a traditional forensic framework. We scope our work for non-corporate owners of embedded devices who are under criminal investigations for a cyber crime.
Autonomous Vehicles' Forensics in Smart Cities

Autonomous vehicles (AVs) are capable of sensing their environment and navigating without any human inputs. When accidents occur between AVs, road infrastructures, or human subjects, liability is decided based on accident forensics. This accident forensics is carried out by the acquisition of sensor data generated within the AVs and through its communication between vehicles to a vehicle (V2V) and vehicle to infrastructure (V2I) with a centralised data hub in smart cities that collects and stores this data thereby aiding the relevant authorities in informed decision making. However, practices mostly employed in extracting this information are unprofessional when compared to other areas of digital forensics. In this paper, we designed and implemented a non-invasive mechanism for the collection and storage of forensic data from AVs within smart cities. This mechanism is efficient, secure, and preserves the privacy of data generated by the AV.
Leveraging an Instance Segmentation Method for Detection of Transparent Materials

Automatic detection of transparent materials (e.g., glass, plastic, etc.) is essential in many computer vision tasks. For example, a robot could use such a system to navigate around transmissive materials or operate tasks with these materials without causing damage. Nevertheless, it is challenging task as such materials exhibit less texture or background scenes dominate visual perception. Existing methods used either handengineered or leaned features to detect and segment transparent objects. We argue that pixel-wise detection and segmentation of transmissive materials improve detection performance and provide the fine-grained information compared to detecting bounding boxes of objects (i.e., localisation task). In this paper, we leverage a robust and state-of-the-art instance segmentation method namely, Mask R-CNN, in order to detect transparent materials. To be specific, we train the model on a new dataset with an evaluation based on publicly available dataset. Experimental results show that the adopted method significantly enhances the performance of transparent material detection. In particular, the resulting binary masks provides the pixel-level information for an improved understanding and analysis of transparency.
Induced Triangular Norms and Negations on Bounded Lattices

Some relevant notions in fuzzy set theory are those of triangular-(t)- norm, t-conorm, and negation, which provide a systematic way of defining set-theoretic operations, or, from other point of view, logical connectives. For instance, the majority of fuzzy implications are directly derived from these operators, so they play a prominent role in fuzzy control theory or in approximate reasoning. This incites the search of suitable t-norms, t-conorms, and negations for solving each specific problem. In this article, we propose a procedure, that we call induction, for designing them on spaces of lattice-valued maps. Concretely, for each family of operators (t-norms, t-conorms, or negations) indexed in the domain set, we may induce an operator of the same kind, so that our method offers a great flexibility in the design task. It may be applied to well-known fuzzy objects as interval-valued or type-2 fuzzy sets. Nevertheless, the theory is formally developed for arbitrary bounded lattices.
Unsupervised Domain Adaptation for Multi-Center Autism Spectrum Disorder Identification

Improving autism spectrum disorder (ASD) diagnosis through effective utilization of multi-center data has attracted increasing attention recently. However, most previous studies do not take the distribution discrepancy among multi-center datasets into consideration, which may degrade the diagnosis performance based on multi-center data. To address this issue, we propose a multi-center domain adaptation (MCDA) method for ASD diagnosis. Most prior researches have explored two learning strategies independently for domain adaptation: feature matching and instance reweighting. In this paper, MCDA is designed to model these two strategies in a unified optimization problem. Specifically, we first choose one center as the target domain and other centers as source domains. Then, we reduce the cross-domain difference by jointly matching the features and reweighting the instances in a principled dimension reduction procedure. Through the procedure our method construct a new feature representation which is invariant to both the distribution difference and the irrelevant instances for both target and source domains. Based on the learned feature representation, we employ the k-nearest neighbor (KNN) algorithm to perform disease classification. Out method has been evaluated on the ABIDE database, and the superior classification results verify that MCDA can significantly outperform competitive methods for multi-center ASD diagnosis.
Periodicity of Grover walks on complete graphs with self-loops

We investigate a periodicity of Grover walk on complete graphs with a self-loop at each vertex. We study an evolution matrix which steps forward a state of probability amplitude vector by using algebraic method. Then we find a periodic behaviour that the probability amplitude vector at each vertex gets back to initial state after some steps. It is shown that Grover walk on complete graphs on n vertices with a self-loop at each vertex is periodic with period 2n.
Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations

We consider the problem of automatically learning the dynamics of physical processes evolving in space and time from incomplete observations. This is a central problem in many fields that remains complicated for large observation spaces and complex dynamics. We propose a data-driven framework, where the system's dynamics are modeled by an unknown time-varying differential equation and the evolution term for the state is estimated from the partially observed data only, using a deep convolutional neural network. Our method yields improvements w.r.t. state-of-the-art recurrent deep network models for the forecast of observations corresponding to complex fluid dynamics. We analyze the latent state representations learned by this model and propose two settings that help interpret the learned states. The model is evaluated on the incompressible Navier Stokes equations.
Gaze-Based Assessment of Dyslexic Students' Motivation within an E-Learning Environment

This paper proposes a gaze-based motivation analysis approach for evaluation of motivational strategies and assessment of motivational factors for students with dyslexia in an e-learning environment. We first collect real-time eye movement data from dyslexic learners during their e-learning practices. We then use statistic tests to evaluate four typical motivational strategies with the eye-tracking data and use logistic regressions for the assessment of motivation. Initial results show that eye-tracking data is effective at evaluating the effects of the studied motivational strategies, and adding eye-tracking features significantly improves the model accuracy which has reached to a correct prediction rate of between 72.0% and 81.3%.
Efficient Heuristic Policy Optimisation for a Challenging Strategic Card Game

Turn-based multi-action adversarial games are challenging scenarios in which each player turn consists of a sequence of atomic actions. The order in which an AI agent runs these atomic actions may hugely impact the outcome of the turn. One of the main challenges of game artificial intelligence is to design a heuristic function to help agents to select the optimal turn to play, given a particular state of the game. In this paper, we report results using the recently developed N-Tuple Bandit Evolutionary Algorithm to tune the heuristic function parameters. For evaluation, we measure how the tuned heuristic function affects the performance of the state-of-the-art evolutionary algorithm Online Evolution Planning. The multi-action adversarial strategy card game Legends of Code and Magic was used as a testbed. Results indicate that the N-Tuple Bandit Evolutionary Algorithm can effectively tune the heuristic function parameters to improve the performance of the agent.
Semanticgan: Generative Adversarial Networks For Semantic Image To Photo-Realistic Image Translation

Generative Adversarial Networks (GANs) have shown remarkable success in Semantic label map to Photo-realistic image Translation (S2PT) task. However, the results of the state-of-the-art approaches are often limited to blurriness and artifacts, and still far from realistic, since these methods lack effective semantic constrains to preserve the semantic information and ignore the structural correlations between the textures. To address those problems, we propose a SemanticGAN to synthesize high resolution image with fine details and realistic textures from the semantic label map. Specifically, we propose a Semantic Information Preserved Loss (SIPL) to maintain semantic information in the process of the generation via a segmentation model. Furthermore, we develop a novel generator to obtain the correlations between the image textures using newly-designed Correlated Residual Block (CRB). Experiments evaluated on Cityscapes dataset show that SemanticGAN outperforms many recent state-of-the-art methods in terms of qualitative and quantitative performance.
Video Analytics Based Identification and Tracking in Smart Spaces

Nowadays, video cameras have become a necessary part of everyday life. These cameras can be found at various places for surveillance, health care, emergencies and disasters. In modern technology, the availability of cheaper and faster communication technologies and efficient algorithms has led to a large-scale deployment of camera networks. Now, Video Content Analysis also referred as Intelligent Video Analytics is used to detect, track and analyze the movement and behaviors of objects and humans. This topic finds various applications because ability to recognize, track and take out information about human activity is very much required. The more technical uses of video content analytics are in face recognition, license plate recognition, tracking a moving object, searching for an object, identifying the geographic location of something, detecting suspicious activities etc. This paper proposes to show and review various algorithms and steps for the applications of the video content analysis that are available in the present date and will also highlight the problems that still exist to find correct results.
Music rhythm tree based partitioning approach to decision tree classifier

Decision tree is a widely used non-parametric technique in machine learning, data mining and pattern recognition. It is simple to understand and interpret, however it faces challenges such as handling higher dimensional and class imbalanced datasets, over-fitting and instability. To overcome some of these issues, vertical partitioning approaches like serial partitioning, theme based partitioning are used in the literature. A vertical partitioning approach divides the feature set into subsets of features (blocks) and makes use of these subsets for subsequent tasks. In this work, we use the ideas of music rhythm tree to propose a novel vertical partitioning technique. It orders the features based on the average correlation strength of the features before partitioning the feature set. The proposed method is proved to be superior by showing an average of 13.8%,6%,9.8%,19.7%,9.4%, and 29.4% higher classification accuracy over C4.5, Random Forest, Bagging, Adaboost, an ensemble technique and a vertical partitioning technique respectively. Our empirical results on 15 datasets demonstrate that the proposed vertical partitioning method is more stable and better in handling class-imbalanced data. Finally, some popular statistical tests are conducted to validate the statistical significance of the results of the proposed method.
A Stacked-Autoencoder Based End-to-End Learning Framework for Decode-and-Forward Relay Networks

In this work, we study an end-to-end deep learning (DL)based constellation design for decode-and-forward (DF) relay network. Firstly, we study both the one-way (OW) and two-way (TW) relaying by interpreting DF relay networks as stacked autoencoders, under Rayleigh fading channels, leading to a performance improvement of 0.5 dB for TWDF networks. Secondly by introducing redundant bits in transmission and reception, we design end-to-end DL-based framework similar to the differential coded modulation for OWDF and coded modulation for TWDF relay networks, under block fading Rayleigh channels and achieve performance gain of 2 dB and 1 dB over conventional method, without using the channel state information knowledge in OWDF networks.
Ham-Sandwich Cuts and Center Transversals in Subspaces

The Ham-Sandwich theorem is a well-known result in geometry. It states that any d mass distributions in $${\mathbb {R}}^d$$ can be simultaneously bisected by a hyperplane. The result is tight, that is, there are examples of $$d+1$$ mass distributions that cannot be simultaneously bisected by a single hyperplane. In this paper we will study the following question: given a continuous assignment of mass distributions to certain subsets of $${\mathbb {R}}^d$$ , is there a subset on which we can bisect more masses than what is guaranteed by the Ham-Sandwich theorem? We investigate two types of subsets. The first type are linear subspaces of $${\mathbb {R}}^d$$ , i.e., k-dimensional flats containing the origin. We show that for any continuous assignment of d mass distributions to the k-dimensional linear subspaces of $${\mathbb {R}}^d$$ , there is always a subspace on which we can simultaneously bisect the images of all d assignments. We extend this result to center transversals, a generalization of Ham-Sandwich cuts. As for Ham-Sandwich cuts, we further show that for $$d-k+2$$ masses, we can choose $$k-1$$ of the vectors defining the k-dimensional subspace in which the solution lies. The second type of subsets we consider are subsets that are determined by families of n hyperplanes in $${\mathbb {R}}^d$$ . Also in this case, we find a Ham-Sandwich-type result. In an attempt to solve a conjecture by Langerman about bisections with several cuts, we show that our underlying topological result can be used to prove this conjecture in a relaxed setting.
An Analysis of Structural Properties in Social Network Links in Educational Data Mining

Social Network Link Analysis is the interaction between the vertices or nodes in the network. These interactions are could be career guidance, common interest, career opportunities; the spark of exchange of interaction becomes belief and knowledge. The interaction becomes complex in the process of time series. To analyze the social network that resulted from these interactions is modeled as a graph structure; it consists of nodes and edges. Various data mining techniques to extract useful information from vast data and support to decide various aspects. It is a dire need of a machine-learning algorithm to assist the knowledge of data mining; we proposed career guidance link analysis in the online social network. In this How students to select the institutions based on the location for their carrier. The proposed study is to evaluate and analyze location-based parameters such as distance, centrality values between the nodes, and residual factors of the parameters. In this paper, we collect the student's data and apply K-Means algorithms using R programming for predicting the ties between the nodes and similarity between the users to make the strong cohesion group. The collected data is categorized using Naïve Bayes classification, and the categorized values are validated 26.4% of students had been accepted the career guidance level, behavioral traits, and organizational traits yielded 38.78 %. Career guidance ties are diffused to 26.3% of Higher education students to choose the choice of institutions. The experimental study of the higher education data results yielded 96.78% accuracy establish to propagate the influence of links to students for the career guidance path.