Reviewerly/exHarmony huggingface dataset:
An unsupervised constrained optimization approach to compressive summarization

Automatic summarization is typically aimed at selecting as much information as possible from text documents using a predefined number of words. Extracting complete sentences into a summary is not an optimal way to solve this problem due to redundant information that is contained in some sentences. Removing the redundant information and compiling a summary from compressed sentences should provide a much more accurate result. Major challenges of compressive approaches include the cost of creating large summarization corpora for training the supervised methods, the linguistic quality of compressed sentences, the coverage of the relevant content, and the time complexity of the compression procedure. In this work, we attempt to address these challenges by proposing an unsupervised polynomial-time compressive summarization algorithm. The proposed algorithm iteratively removes redundant parts from original sentences. It uses constituency-based parse trees and hand-crafted rules for generating elementary discourse units (EDUs) from their subtrees (standing for phrases) and selects ones with a sufficient tree gain. We define a parse tree gain as a weighted function of its node weights, which can be computed by any extractive summarization model capable of assigning importance weights to terms. The results of automatic evaluations on a single-document summarization task confirm that the proposed sentence compression procedure helps to avoid redundant information in the generated summaries. Furthermore, the results of human evaluations confirm that the linguistic quality—in terms of readability and coherency—is preserved in the compressed summaries while improving their coverage. However, the same evaluations show that compression in general harms the grammatical correctness of compressed sentences though, in most cases, this effect is not significant for the proposed compression procedure.
FaceGenderID: Exploiting Gender Information in DCNNs Face Recognition Systems

This paper addresses the effect of gender as a covariate in face verification systems. Even though pre-trained models based on Deep Convolutional Neural Networks (DCNNs), such as VGG-Face or ResNet-50, achieve very high performance, they are trained on very large datasets comprising millions of images, which have biases regarding demographic aspects like the gender and the ethnicity among others. In this work, we first analyse the separate performance of these state-of-the-art models for males and females. We observe a gap between face verification performances obtained by both gender classes. These results suggest that features obtained by biased models are affected by the gender covariate. We propose a gender-dependent training approach to improve the feature representation for both genders, and develop both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model. Our results show significant and consistent improvements in face verification performance for both genders, individually and in general with our proposed approach. Finally, we announce the availability (at GitHub) of the FaceGenderID DCNNs models proposed in this work, which can support further experiments on this topic.
Tracking Persons-of-Interest via Unsupervised Representation Adaptation

Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often can appear drastically different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up. Existing multi-target tracking methods often use low-level features which are not sufficiently discriminative for identifying faces with such large appearance variations. In this paper, we tackle this problem by learning discriminative, video-specific face representations using convolutional neural networks (CNNs). Unlike existing CNN-based approaches which are only trained on large-scale face image datasets offline, we automatically generate a large number of training samples using the contextual constraints for a given video, and further adapt the pre-trained face CNN to the characters in the specific videos using discovered training samples. The embedding feature space is fine-tuned so that the Euclidean distance in the space corresponds to the semantic face similarity. To this end, we devise a symmetric triplet loss function which optimizes the network more effectively than the conventional triplet loss. With the learned discriminative features, we apply an EM clustering algorithm to link tracklets across multiple shots to generate the final trajectories. We extensively evaluate the proposed algorithm on two sets of TV sitcoms and YouTube music videos, analyze the contribution of each component, and demonstrate significant performance improvement over existing techniques.
Analysis of the Selection Factor of Online Transportation in the VIKOR Method in Pematangsiantar City

Abstract Transportation plays an important role in helping every community activity and also has an important function in economic, social and developmental development. Online transportation provides alternative transportation solutions in the middle of the density of vehicles to be fast and able to reach places that are likely to be inaccessible to other public vehicles. People who usually use public transport or conventional taxi services have begun to switch to online-based taxis that are considered cheaper and more practical. Market research institutes in Southeast Asia show that 250 million Indonesians are quickly adapting the growth of application-based transportation (taxis and ojek online) to facilitate their lives. Various factors that influence people prefer online transportation compared to public transport or conventional taxis. The purpose of the study is to determine the main factors that people prefer to use Oline transportation. The study used the VIKOR method system which obtained the results based on the VIKOR index value where alternative A1: Easy and Safe (VIKOR index value 0) as a rank one and alternative A2: Price (VIKOR index value 0.1809520) as the second rank.
White-Box Traceable Ciphertext-Policy Attribute-Based Encryption in Multi-Domain Environment

In a ciphertext-policy attribute-based encryption (CP-ABE) system, the decryption keys are only related to attributes shared by multiple users and do not contain any identity information of their original holders.Hence, if a decryption key is leaked, there is no feasible method to trace the suspicious user.The user tracing problem has become an obstacle to the adoption of CP-ABE in practice.In order to address it, some traceable/accountable CP-ABE schemes have been established.However, considering the user tracing problem in multi-domain environment will face new challenges.Multi-domain environments usually have a two-layer structure, domains and intradomain users.When tracing a user, we should first trace the domain where the user is located, and then trace the user in the domain.Unfortunately, the existing traceable CP-ABE schemes only focus on one level of users tracing, and are not suitable for the two-layer structure of multi-domain environments.Therefore, a white-box traceable CP-ABE scheme in multi-domain environment is proposed in this paper.The proposed scheme obtains a so-called two-layer tracing.At the domain level, a short signature technique is used to prevent an attacker from forging the tracing parameter and realize the traceability for domains.Linkable ring signature technology is introduced at the user level to provide a tracing method for users by utilizing the linkability of the signature.The two signature structures are reasonably embedded in a user private key to support two-layer white-box tracing at both domains and intradomain users.The proposed scheme supports any monotone access structures and has full security against chosen plaintext attack in the standard model.Compared with related schemes, the asymptotic communication cost and the asymptotic computation cost of the proposed scheme are relatively low.These advantages make the proposed scheme more practical for solving the user tracing problem in multi-domain environment. INDEX TERMS Ciphertext-policy attribute-based encryption,
Formalizing Constructive Cryptography using CryptHOL

Computer-aided cryptography increases the rigour of cryptographic proofs by mechanizing their verification.Existing tools focus mainly on game-based proofs, and efforts to formalize composable frameworks such as Universal Composability have met with limited success.In this paper, we formalize an instance of Constructive Cryptography, a generic theory allowing for clean, composable cryptographic security statements.Namely, we extend CryptHOL, a framework for game-based proofs, with an abstract model of Random Systems and provide proof rules for their equality and composition.We formalize security as a special kind of system construction in which a complex system is built from simpler ones.As a simple case study, we formalize the construction of an information-theoretically secure channel from a key, a random function, and an insecure channel.
Detection of Fake Reviews: Analysis of Sellers’ Manipulation Behavior

Online reputation systems play an important role in reducing consumers’ purchase uncertainty in online shopping. However, some sellers manipulate reviews for their own interests, which reduces the effectiveness of the reputation system. Unlike the previous studies, which focus on features of reviews and reviewers, this study establishes a game model to analyze sellers’ manipulation behavior and identifies what kind of sellers or under what scenario sellers are motivated to manipulate reviews. Our study provides a new perspective for platform to detect fake reviews and helps consumers to make good use of online reviews without getting trapped in some sellers’ fraudulent manipulation.
Adaptive Anchor for Fast Object Detection in Aerial Image

Object detection in aerial images is an important task for many applications such as reconnaissance, surveillance, search, and rescue. At present, convolution neural network-based aerial image object detection algorithms mainly focus on rotation invariance and scale invariance, but ignore an important characteristic of the aerial image that the image captured height is an important prior knowledge. At the same captured height, the target has a clear scale range. In this letter, a scale-aware network is proposed to determine the scale of predefined anchors, which can effectively reduce the scale search range, reduce the risk of overfitting, and improve the detection accuracy and speed in aerial images. Experiments on the VisDrone data set show that the proposed method can not only improve the detection speed by 18% but also improve the average accuracy by 1.6%.
Depth Map Estimation for Free-Viewpoint Television and Virtual Navigation

The paper presents a new method of depth estimation dedicated for free-viewpoint television (FTV). The estimation is performed for segments and thus their size can be used to control a trade-off between the quality of depth maps and the processing time of their estimation. The proposed algorithm can take as its input multiple arbitrarily positioned views which are simultaneously used to produce multiple inter view consistent output depth maps. The presented depth estimation method uses novel parallelization and temporal consistency enhancement methods that significantly reduce the processing time of depth estimation. An experimental assessment of the proposals has been performed, based on the analysis of virtual view quality in FTV. The results show that the proposed method provides an improvement of the depth map quality over the state of-the-art method, simultaneously reducing the complexity of depth estimation. The consistency of depth maps, which is crucial for the quality of the synthesized video and thus the quality of experience of navigating through a 3D scene, is also vastly improved.
A Comparative study of applications of Game Theory in Cyber Security and Cloud Computing.

Game theory is a mathematical model which deals with interactions between various entities by analyzing the strategies and choices. In today's world, Game Theory is being extensively used in fields like economics, sociology, political science, etc. due to its versatile nature and applications in myriad of conflicts and problems. The application of game theory has been extended to computer science also due to its versatility and robustness. In this paper we have discussed the applications of game theory in fields of cyber security and cloud computing along with the comparative study of different game models used in their respective fields. We have also discussed about Compositional Game Theory and its applications for solving complex problems.
Automatic Irregularity-Aware Fine-Grained Workload Partitioning on Integrated Architectures

The integrated architecture that features both CPU and GPU on the same die is an emerging and promising architecture for fine-grained CPU-GPU collaboration. However, the integration also brings forward several programming and system optimization challenges, especially for irregular applications such as graph processing. The complex interplay between heterogeneity and irregularity leads to very low processor utilization of running irregular applications on integrated architectures. Furthermore, fine-grained co-processing on the CPU and GPU is still an open problem. Particularly, in this paper, we show that the previous workload partitioning for CPU-GPU co-processing is far from ideal in terms of resource utilization and performance. To solve this problem, we propose a system software called FinePar, which considers architectural differences of the CPU and GPU and leverages fine-grained collaboration enabled by integrated architectures. Through irregularity-aware performance modeling and online auto-tuning, FinePar partitions irregular workloads and achieves both device-level and thread-level load balance. We evaluate FinePar with eight irregular applications in graphs and sparse matrices on two integrated architectures and compare it with state-of-the-art partitioning approaches. Results show that FinePar demonstrates better resource utilization and achieves an average of 1.6X speedup over the optimal coarse-grained partitioning method.
Evaluating Defensive Distillation for Defending Text Processing Neural Networks Against Adversarial Examples

Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for many tasks such as image and text classification, especially as research shows that many adversarial examples are transferable between different classifiers. In this work, we evaluate the performance of a popular defensive strategy for adversarial examples called defensive distillation, which can be successful in hardening neural networks against adversarial examples in the image domain. However, instead of applying defensive distillation to networks for image classification, we examine, for the first time, its performance on text classification tasks and also evaluate its effect on the transferability of adversarial text examples. Our results indicate that defensive distillation only has a minimal impact on text classifying neural networks and does neither help with increasing their robustness against adversarial examples nor prevent the transferability of adversarial examples between neural networks.
Tonight We Improvise!

We present an exploratory study based on dance improvisation to explore embodied interaction between human dancers and a mobile robot. Following extensive iterations with expert dancers, we developed a sequence of basic motion algorithms based on improvisation exercises to generate three unique, original performances between a robot and human performers trained in various dance styles. We developed a novel method for tracking the dancers in real time using inputs to generate choreography for the non-anthropomorphic robot. Although the motion algorithms were identical, the individual dancers generated vastly different performances and elicited unexpected motions and choreographies. We summarize our study and identify some challenges of devising performances between robots and humans, and outline future work to experiment with more advanced algorithms.
Incorporating token-level dictionary feature into neural model for named entity recognition

For named entity recognition, incorporating dictionary feature into the deep neural model can obtain better performance. However, the behavior of deep models which incorporate dictionary features is not clear because analyzing this behavior is difficult. Besides, using the dictionary features which depend on a dictionary may train a poor model. In this work, we present an incorporating token-level dictionary feature method which use a labeled dataset rather than an external dictionary. This approach makes it easier to quantify the effect of dictionary features and decouples dictionary features from the external dictionary during the training stage. Additionally, we conduct several experiments which consist of three parts. First, we show the effectiveness of our proposed method. Second, we study the behavior of incorporating dictionary features from two perspectives: during the training and the testing stage. Third, we research the embedding of dictionary features.
Techno‐economic analysis of 5G immersive media services in cloud‐enabled small cell networks: The neutral host business model

Abstract Fifth generation (5G) envisages a “hyperconnected society” with an enormous number of interconnected devices, anywhere and at any time. Edge computing plays a pivotal role in this vision, enabling low latency, large traffic volumes, and improved quality of experience. The advent of 5G and edge computing encourages vertical industries to develop innovative services, which can meet the challenging demands coming from consumers. However, economic feasibility is the ultimate factor that determines the viability of a new service. Hence, effective techniques for the economic assessment of such services are needed. This paper analyzes the provision of immersive media services in crowded events, through a cloud‐enabled small cell network owned by a neutral host, and offered in multitenancy to different mobile network operators. We initially develop a planning model to predict the required compute, storage, and radio resources. Taking into account dynamic factors such as service penetration and price evolution, we then provide a number of economic indices, such as net present value, internal rate of return, and expected payback period to assess the viability of a potential investment in a 5G infrastructure for immersive media services. The presented analysis will guide small cell network operators in the provision of 5G innovative media services.
FlexTouch

In this paper, we present FlexTouch, a technique that enables large-scale interaction sensing beyond the spatial constraints of capacitive touchscreens using passive low-cost conductive materials. This is achieved by customizing 2D circuit-like patterns with an array of conductive strips that can be easily attached to the sensing nodes on the edge of the touchscreen. FlexTouch requires no hardware modification, and is compatible with various conductive materials (copper foil tape, silver nanoparticle ink, ITO frames, and carbon paint), as well as fabrication methods (cutting, coating, and ink-jet printing). Through a series of studies and illustrative examples, we demonstrate that FlexTouch can support long-range touch sensing for up to 4 meters and everyday object presence detection for up to 2 meters. Finally, we show the versatility and feasibility of FlexTouch through applications such as body posture recognition, human-object interaction as well as enhanced fitness training experiences.
Spectral mapping theorem of an abstract quantum walk

Given two Hilbert spaces, $$\mathcal {H}$$ and $$\mathcal {K}$$ , we introduce an abstract unitary operator U on $$\mathcal {H}$$ and its discriminant T on $$\mathcal {K}$$ induced by a coisometry from $$\mathcal {H}$$ to $$\mathcal {K}$$ and a unitary involution on $$\mathcal {H}$$ . In a particular case, these operators U and T become the evolution operator of the Szegedy walk on a graph, possibly infinite, and the transition probability operator thereon. We show the spectral mapping theorem between U and T via the Joukowsky transform. Using this result, we have completely determined the spectrum of the Grover walk on the Sierpiński lattice, which is pure point and has a Cantor-like structure.
Multi-object Detection Method based on YOLO and ResNet Hybrid Networks

As a sub-discipline related to artificial intelligence, machine vision has become a hotspot in recent years. Object detection, as part of machine vision, has been widely used in various fields. However, the existing object detection methods based on deep learning theory cannot accurately recognize multiple objects. Thus, in this paper we propose a new hybrid network based on YOLO and ResNet (Yolo-resnet) for multi-object detection. Our method integrates ResNet into the feature extraction of the YOLOv3 framework and the detection results demonstrate that our hybrid network is efficient for detecting multi-objects from an image of complex natural scenes.
Exploring the cognitive process for service task in smart home: A robot service mechanism

Smart home (SH) has been widely viewed as a promising paradigm to enhance the quality of life by providing context-aware services. Although the remarkable progress in SH has been made, it still remains a challenging problem to actively discover and provide complex service tasks in a proper way, especially bridging the gap between the complex tasks and the executable task descriptions by robot. To address this problem, this paper explores a robot service mechanism based on system cooperation in SH. Through the combination of intelligent space and robot, a system architecture of SH is presented, where the abilities of environmental perception and service execution can be effectively improved. For supporting the proposed system architecture and service task, a knowledge-based context model is proposed to address the data heterogeneity issue. Then the robot service mechanism of how to actively discover and provide the service task in the right way is fully revealed, the purpose of which is to provide a feasible solution for improving the quality of life. In particular, the comprehensive cognitive process of service task, including task cognition, task inference, task planning and task assessment, and autonomous navigation of service robot are exposed. The comprehensive experimental evaluations are conducted in the real-world, and the results demonstrate the feasibility and effectiveness of the presented schemes.
Equity market and money supply spillovers and economic growth in BRICS economies: A global vector autoregressive approach

Previous studies report that private credit as a proxy of financial development contributes to economic growth in BRICS economies. This paper employs three additional measures of financial development, namely equity market, money supply and market capitalization, and further investigates cross-country evidence on the impact of equity market and money supply spillovers on economic growth in BRICS economies. Utilizing a Global Vector Autoregressive (GVAR) framework and quarterly data from 1989Q1 and to 2012Q4 from BRICS economies, we find that equity market and money supply variables do not predict the contributions of financial development in each BRICS member in boosting economic growth in the other member countries. However, market capitalization significantly influences economic growth. These results suggest that, besides private credit, market capitalization is another key channel of promoting growth in individual economies and the region. Policy implications of the findings are discussed.