Reviewerly/exHarmony huggingface dataset:
SPGD_HIN: Spammer Group Detection based on Heterogeneous Information Network

Online stores and e-commerce platforms have become increasingly popular in recent years, and a reasonable approach to compare the available products is to use comments or feedbacks written by other online users for each product. Therefore, these platforms can be a great opportunity for spammers to promote or demote their target products with fake reviews. So far, there is plenty of studies done with the purpose of distinguishing spam reviews or spammers from genuine ones, but it should not be neglected that often spammers work in collusion with each other to control the rating score of a product more naturally. Hence, this article focuses on the latter aspect i.e., review spammer group detection. In most of the previous works, Frequent Item set Mining (FIM) is applied in the early stage to find candidate groups and then an unsupervised ranking procedure is done based on some predefined features. Although, FIM methods mostly suffer from threshold setting, i.e., using low support values causes inefficiency and high support values ignore some useful patterns. Furthermore, instead of unsupervised methods, semi-supervised ones which don't need many labeled data, can improve the accuracy of detection greatly. In this article, we tackle the above-mentioned challenges taking advantage of some labeled instances in a Heterogeneous Information Network (HIN). Using a HIN can preserve the semantics between different kinds of nodes in the network. Also, we extract candidate groups using spammer behaviors and their relations which makes it a robust approach when spammers decide to be more intelligent. Experiments on a real-life Yelp dataset show the efficiency of our approach.
An Analysis of Control Parameter Importance in the Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) is a stochastic search algorithm based on the social dynamics of a flock of birds. The performance of the PSO algorithm is known to be sensitive to the values assigned to its control parameters, and appropriate tuning of these control parameters can greatly improve performance. This paper employs function analysis of variance (fANOVA) to quantify the importance of each of the three conventional PSO control parameters, namely the inertia weight ( $$\omega $$ ), the cognitive acceleration coefficient ( $$c_1$$ ), and the social acceleration coefficient ( $$c_2$$ ), according to their respective variances associated with the fitness. Results indicate that the inertia value, $$\omega $$ , has the greatest sensitivity to its assigned value and thus is the most important parameter to tune when optimizing PSO performance for low dimensional problems.
A Crowd Behavior Identification Method Combining the Streakline With the High-Accurate Variational Optical Flow Model

A crowd behavior identification method is proposed by combining the streakline based on fluid mechanics with a high-accurate variational optical flow model in this paper.Firstly, calculated by the high-accurate variational optical flow model, the streaklines are used to acquire the crowd motion trajectory information.The angular histogram and the regions of interest in the scenes are obtained by calculating and clustering the dasymetric dot maps of the starting and ending points of the trajectory, and then, combining the dasymetric dot map and angular histogram information to analyze whether there are specific crowd behaviors in the regions of interest, and thus to identify different types of crowd behavior in such scene.Finally, experimental comparison and analysis are made to verify the effectiveness and accuracy of the method proposed in this paper.
The Domain Transform Solver

We present a novel framework for edge-aware optimization that is an order of magnitude faster than the state of the art while maintaining comparable results. Our key insight is that the optimization can be formulated by leveraging properties of the domain transform, a method for edge-aware filtering that defines a distance-preserving 1D mapping of the input space. This enables our method to improve performance for a wide variety of problems including stereo, depth super-resolution, render from defocus, colorization, and especially high-resolution depth filtering, while keeping the computational complexity linear in the number of pixels. Our method is highly parallelizable and adaptable, and it has demonstrable linear scalability with respect to image resolutions. We provide a comprehensive evaluation of our method w.r.t speed and accuracy for a variety of tasks.
User Test: How Many Users Are Needed to Find the Psychophysiological Pain Points in a Journey Map?

The objective of this research is to investigate how many users are needed in usability testing to identify psychophysiological pain points (PPP) experienced by users during a human-computer interaction (HCI). Fifteen subjects were tested in a new user training context and results show that out of 15 participants, 82% of the total PPP were experienced after 9 participants. Calculations using 1000 trials of random orders were then performed to demonstrate the independence of the order. This research provides guidelines about what could be an ideal sample size for user test using psychophysiological measures.
Design of the research problem statement

Abstract This paper aims to provide an understanding of designing a problem statement that must be present in every research proposal or other scientific work. A study, from proposals to scientific papers, requires a problem statement. The design of the program statement is basically always present from research interests. Objectives as a translation of the problem statement will be described through a methodology that matches the answers to be given to the interests. In the industry 4.0 era, problem statement in research prioritize innovation or change to improve welfare.
Learning Sports Camera Selection From Internet Videos

This work addresses camera selection, the task of predicting which camera should be on air from multiple candidate cameras for soccer broadcast. The task is challenging because of the scarcity of learning data with all candidate views. Meanwhile, broadcast videos are freely available on the Internet (e.g. Youtube). However, these videos only record the selected camera views, omitting the other candidate views. To overcome this problem, we first introduce a random survival forest (RSF) method to impute the incomplete data effectively. Then, we propose a spatial-appearance heatmap to describe foreground objects (e.g. players and balls) in an image. To evaluate the performance of our system, we collect the largest-ever dataset for soccer broadcasting camera selection. It has one main game which has all candidate views and twelve auxiliary games which only have the broadcast view. Our method significantly outperforms state-of-the-art methods on this challenging dataset. Further analysis suggests that the improvement in performance is indeed from the extra information from auxiliary games.
Noise Effects and Perfect Controlled Remote State Preparation

We investigate how the efficiency is affected on the noise environments in the controlled remote state preparation protocol, where the several realistic scenarios, i.e., a part or all of the qubits are subjected to the same or different types of noise, are considered. We find that more noise or less entanglement of qubits environment lead to more efficiency in terms of average fidelity. We show that it is better way to subject the qubits in different noise channels in order to increase the fidelity of the controlled remote state preparation protocol. By using a non-maximally three-qubit pure entangled state as quantum channel, furthermore, we could realize a perfect controlled remote state preparation by choosing the right noisy environments and adjusting their relations in terms of noisy rates.
Is current institutional quality linked to legal origins and disease endowments? Evidence from Africa

This paper empirically assesses if and to what extent cross-country differences in institutions inherited from the past affect current institutional quality in Africa. Specifically, the work evaluates if legal origins and disease endowments explain cross-country differences in the quality of contemporary institutions that are widely considered to be important for financial system development and other economic outcomes, such as those related to creditor rights protection and the credit information infrastructure, as well as the judicial, legal property and insolvency systems. Empirical tests are carried out on a sample of 46 African former European colonies with data on current institutional quality from 2004 to 2013. The findings reveal that the legal origins hypothesis receives strong support in the data: historical factors, rooted in legal origins, have long-lasting effects on current institutional quality in the African context. Mixed evidence is instead found for the endowment view.
On Rectangular Unimodular Matrices over the Algebraic Integers

Let $n$ and $m$ be positive integers such that $n<m$. In this paper we compute the density of rectangular unimodular $n \times m$ matrices over the ring of algebraic integers of a number field.MSC codesnumber fieldsdensitiesunimodular matricesMSC codes11M5011R4511C20
Learning Common Representation From RGB and Depth Images

We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.
Crime Knowledge Extraction

In this paper, we present CRIKE, a data-science approach to automatically detect concrete applications of legal abstract terms in case-law decisions. To this purpose, CRIKE relies on the use of the LATO ontology where legal abstract terms are properly formalized as concepts and relations among concepts. Using LATO, CRIKE aims at discovering how and where legal abstract terms are applied by judges in their legal argumentation. Moreover, we detect the terminology used in the text of case-law decisions to characterize concrete abstract-term instances. A case-study on a case-law decisions dataset provided by the Court of Milan, Italy, is also discussed.
Reed–Solomon Codes Over Small Fields With Constrained Generator Matrices

We give constructions of some special cases of [n, k] Reed-Solomon codes over finite fields of size at least n and n + 1 whose generator matrices have constrained support. Furthermore, we consider a generalization of the GM-MDS conjecture proposed by Lovett in 2018. We show that Lovett's conjecture is false in general and we specify when the conjecture is true.
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM

Understanding the bottlenecks in implementing stochastic gradient descent (SGD)-based distributed support vector machines (SVM) algorithm is important in training larger data sets. The communication time to do the model synchronization across the parallel processes is the main bottleneck that causes inefficiency in the training process. The model synchronization is directly affected by the mini-batch size of data processed before the global synchronization. In producing an efficient distributed model, the communication time in training model synchronization has to be as minimum as possible while retaining a high testing accuracy. The effect from model synchronization frequency over the convergence of the algorithm and accuracy of the generated model must be well understood to design an efficient distributed model. In this research, we identify the bottlenecks in model synchronization in parallel stochastic gradient descent (PSGD)-based SVM algorithm with respect to the training model synchronization frequency (MSF). Our research shows that by optimizing the MSF in the data sets that we used, a reduction of 98% in communication time can be gained (16x - 24x speed up) with respect to high-frequency model synchronization. The training model optimization discussed in this paper guarantees a higher accuracy than the sequential algorithm along with faster convergence.
Bayesian Penalized Method for Streaming Feature Selection

The online feature selection with streaming features has become more and more important in recent years. In contrast to standard feature selection method, streaming feature selection method can select feature dynamically without exploring full feature space. In some applications, when the information of all features is unknown in advance, standard feature selection methods cannot perform well in this setting. Moreover, in ultrahigh dimensional data analysis, especially considering interaction effects between features, the streaming feature selection method enjoy computational feasibility. In this paper, we proposed a Bayesian penalized method for streaming feature selection problem. The proposed approach adopts Bayesian regularization into penalized model which can adaptively estimates regularization parameter based on the coefficients of current model. Comparing with many existing streaming feature selection methods, our method can work for more general case of predictive model. The proposed method is evaluated extensively on various high-dimensional datasets. The experimental results show that the algorithm is competitive with many existing streaming and traditional feature selection algorithms.
Differentially-Private Two-Party Egocentric Betweenness Centrality

We describe a novel protocol for computing the egocentric betweenness centrality of a node when relevant edge information is spread between two mutually distrusting parties such as two telecommunications providers. While each node belongs to one network or the other, its ego network might include edges unknown to its network provider. We develop a protocol of differentially-private mechanisms to hide each network's internal edge structure from the other; and contribute a new two-stage stratified sampler for exponential improvement to time and space efficiency. Empirical results on several open graph data sets demonstrate practical relative error rates while delivering strong privacy guarantees, such as 16% error on a Facebook data set.
An adaptive neuro-complex-fuzzy-inferential modeling mechanism for generating higher-order TSK models

In this paper, we present an adaptive neuro-complex-fuzzy-inferential modeling mechanism (ANCFIMM) for generating higher-order Takagi-Sugeno-Kang (TSK) models, and exploit its split complex-valued gradient descent algorithm (SCGDA). ANCFIMM has five layers, among which the Gaussian membership layer casts the M-dimensional complex-valued imported features to a K-dimensional real number field. Hence, we use the splitting strategy to acquire the gradients of the real-valued function in deriving the learning algorithm of ANCFIMM. The monotonicity and deterministic convergence of SCGDA are demonstrated and proved in detail. Experiments are also implemented to manifest the properties of ANCFIMM on a series of data sets, which effectively validate the theoretical observations.
Attention Based Natural Language Grounding by Navigating Virtual Environment

In this work, we focus on the problem of grounding language by training an agent to follow a set of natural language instructions and navigate to a target object in an environment. The agent receives visual information through raw pixels and a natural language instruction telling what task needs to be achieved and is trained in an end-to-end way. We develop an attention mechanism for multi-modal fusion of visual and textual modalities that allows the agent to learn to complete the task and achieve language grounding. Our experimental results show that our attention mechanism outperforms the existing multi-modal fusion mechanisms proposed for both 2D and 3D environments in order to solve the above-mentioned task in terms of both speed and accuracy. We show that the learnt textual representations are semantically meaningful as they follow vector arithmetic in the embedding space. The effectiveness of our attention approach over the contemporary fusion mechanisms is also highlighted from the textual embeddings learnt by the different approaches. We also show that our model generalizes effectively to unseen scenarios and exhibit zero-shot generalization capabilities both in 2D and 3D environments. The code for our 2D environment as well as the models that we developed for both 2D and 3D are available at https://github.com/rl-lang-grounding/rl-lang-ground.
Towards Eco-Centric Interaction: Urban Playful Interventions in the Anthropocene

The twin crises of nature and climate is supported by overwhelming scientific evidence and increasing public concern about long-term and potentially irreversible consequences of the Anthropocene. People and wildlife living today, as well as future generations, are at risk unless urgent action is taken to reverse the loss of plants, insects and other creatures on which we depend for food, clean water, and a stable climate. As urban dwellings become the main concentration of citizens, we thought of addressing these issues through a series of urban interventions aiming at educating and enhancing the user’s biodiversity and sustainability awareness through play and reflection. We provide a list of preliminary insights, discussed, and shared trying to frame how these new interventions can contribute to a panorama where playful interactions in smart cities can inspire sustainable and respectful attitudes towards nature. We conclude with a constructive conversation about playful urban approaches aimed at understanding how the interaction process could be re-centered to promote environmental protection and ecological consciousness on the part of technology users. Our case studies strive to reconcile concepts and theories, about ecological computing, more than human design, biodiversity actions and nature awareness and conservation in urban settings, for the design of urban playful and enjoyable systems that promote environmental protection and ecological consciousness on the part of technology users.
A survey on quality-assurance approximate stream processing and applications

The massive growth of data now being made available from a variety of sources leads to an increased demand for fast data processing to extract value from the data. In data streams, processing data requires computational power and data storage capabilities that have not kept pace with the data collection abilities. For these reasons approximate computations have been developed to handle both computational issues as well as the storage issues especially related to real-time data streams. In this paper, we first propose a comprehensive study of approximate computing techniques for data streams. We classify common approximate techniques as data-driven and computing-driven methods, and also discuss the combination of the two methods in emerging distributed processing environments. Based on existing approximate methods, we then detail the research on data quality management including quality evaluation and monitoring. The challenges are grouped into several research themes including pre-evaluation, data learning, approximation processing, and quality measurement. The aim of the paper is to provide researchers with a guide for how to make effective systematic strategies for approximate stream processing.
3D printing for mixed reality hands-on museum exhibit interaction

This work presents a combination of 3D printing with mixed reality to use the results in the context of museum exhibitions or for cultural heritage. While now priceless artefacts are encased in glass, kept safe and out of reach of the visitors, we present a new pipeline which would allow visitors hands-on interaction with realistic 3D printed replicas of the artefacts which are then digitally augmented to have the genuine artefacts' appearances.
Deep reinforcement learning-driven intelligent panoramic video bitrate adaptation

Online panoramic video has recently gained enormous popularity. Tile-based adaptive streaming is a promising method to deliver a panoramic video for the sake of bandwidth saving. However, it's challenging to estimate the user's field of view (FoV) and deliver the optimal bitrate due to the dynamic user behavior and time-varying network. In this paper, we propose a novel approach to delivering panoramic video. Specifically, a long short-term memory (LSTM) model is used to estimate the FoV in the next few seconds. Our quality adaptation policy is based on a deep reinforcement learning (DRL) agent, which is able to intelligently adapt its bitrate selection policy to different environments. We have implemented a prototype of this system, which outperforms other existing panoramic video streaming frameworks by 12% in quality of experience (QoE) after getting converged in a wide range of environment metrics, and achieves the best performance.
CASO: Cost-Aware Secure Outsourcing of General Computational Problems

Computation outsourcing is an integral part of cloud computing. It enables end-users to outsource their computational tasks to the cloud and utilize the shared cloud resources in a pay-per-use manner. However, once the tasks are outsourced, the end-users will lose control of their data, which may result in severe security issues especially when the data is sensitive. To address this problem, secure outsourcing mechanisms have been proposed to ensure security of the end-users' outsourced data. In this paper, we investigate outsourcing of general computational problems which constitute the mathematical basics for problems emerged from various fields such as engineering and finance. To be specific, we propose affine mapping based schemes for the problem transformation and outsourcing so that the cloud is unable to learn any key information from the transformed problem. Meanwhile, the overhead for the transformation is limited to an acceptable level compared to the computational savings introduced by the outsourcing itself. Furthermore, we develop cost-aware schemes to balance the trade-offs between end-users' various security demands and computational overhead. We also propose a verification scheme to ensure that the end-users will always receive a valid solution from the cloud. Our extensive complexity and security analysis show that our proposed Cost-Aware Secure Outsourcing (CASO) scheme is both practical and effective.
Topological complexity of the work map

We introduce the topological complexity of the work map associated to a robot system. In broad terms, this measures the complexity of any algorithm controlling, not just the motion of the configuration space of the given system, but the task for which the system has been designed. From a purely topological point of view, this is a homotopy invariant of a map which generalizes the classical topological complexity of a space.