Reviewerly/exHarmony huggingface dataset:
A survey of domains in workflow scheduling in computing infrastructures: Community and keyword analysis, emerging trends, and taxonomies

Workflows are prevalent in today’s computing infrastructures as they support many domains. Different Quality of Service (QoS) requirements of both users and providers makes workflow scheduling challenging. Meeting the challenge requires an overview of state-of-art in workflow scheduling. Sifting through literature to find the state-of-art can be daunting, for both newcomers and experienced researchers. Surveys are an excellent way to address questions regarding the different techniques, policies, emerging areas, and opportunities present, yet they rarely take a systematic approach and publish their tools and data on which they are based. Moreover, the communities behind these articles are rarely studied. We attempt to address these shortcomings in this work. We introduce and open-source an instrument used to combine and store article meta-data. Using this meta-data, we characterize and taxonomize the workflow scheduling community and four areas within workflow scheduling: (1) the workflow formalism, (2) workflow allocation, (3) resource provisioning, and (4) applications and services. In each characterization, we obtain important keywords overall and per year, identify keywords growing in importance, get insight into the structure and relations within each community, and perform a systematic literature survey per part to validate and complement our taxonomies
Content-Based Management of Human Motion Data: Survey and Challenges

Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing.Content-based processing of skeleton data has developed rapidly in recent years, focusing mainly on specialized prototypes with limited consideration of generic data management possibilities.In this survey article, we synthesize and categorize the existing approaches and outline future research challenges brought by the increasing availability of human motion data.In particular, we first discuss the problems of suitable representation and segmentation of continuous skeleton data obtained from various sources.Then, we concentrate on comparison models for assessing the similarity of time-restricted pieces of motions, as required by any content-based management operation.Next, we review the techniques for evaluating similarity queries over collections of motion sequences and filtering query-relevant parts from continuous motion streams.Finally, we summarize the usability of existing techniques in perspective application domains and discuss the new challenges related to current technological and infrastructural developments.We especially assess the existing techniques from the perspective of scalability and propose future research directions for dealing with large and diverse volumes of skeleton data.
Temporal Knowledge Graph Embedding for Effective Service Recommendation

Over the last decade, service selection and recommendation had been two strongly related service filtering steps. While service selection aims to filter the best available services according to QoS and contextual criteria, service recommendation refines the selection results by taking into account additional criteria, such as users feedbacks and ratings, similarities between users tastes, etc. However, the ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-aware recommender systems</i> . In this work, we propose a service recommendation method that takes advantage of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal knowledge graphs</i> . As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining. Experimental studies have proven the effectiveness and accuracy of our approach, compared to traditional TASR methods and time-unaware KG-based recommendation.
Game Strategies for Physical Robot Soccer Players: A Survey

Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system—ranging from hardware to high-level strategy learning. Among these competitions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoboCup</i> has a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoboCup</i> framework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games.
Quantum-sapiens: the quantum bases for human expertise, knowledge, and problem-solving

Despite the great promises and potential of quantum computing, the full range of possibilities and practical applications is not yet clear. In this contribution, we highlight the potential for quantum theories and computation to reignite the art and science of expert systems and knowledge engineering. With their grounding in uncertainty and unpredictability, quantum concepts are able to expand theoretical and practical boundaries of research and exploration. We demonstrate the advantages of using quantum logic and computation when applying expert knowledge such as the ability to ask imprecise (inherently probabalistic) queries and do so efficiently through large and complicated computational spaces by leveraging quantum concepts such as superposition, entanglement, parallelism, and interference to sustain and manipulate networks of quantum qubits. This, we argue, can provide non-insignificant computational speedups and enable more complex data representations and analysis. We then point the keen reader towards three practical applications and provide some evidence for their quantum advantages – Scientific Discovery, Creativity and Breakthrough Thinking, Complex Systems, and Future of Sports – that we regard as largely unexplored in popular accounts of quantum theory; as such, we suggest these are low hanging fruit for the more-immediately feasible applications of quantum technologies.
Order-C Secure Multiparty Computation for Highly Repetitive Circuits

Running secure multiparty computation (MPC) protocols with hundreds or thousands of players would allow leveraging large volunteer networks (such as blockchains and Tor) and help justify honest majority assumptions. However, most existing protocols have at least a linear (multiplicative) dependence on the number of players, making scaling difficult. Known protocols with asymptotic efficiency independent of the number of parties (excluding additive factors) require expensive circuit transformations that induce large overheads. We observe that the circuits used in many important applications of MPC such as training algorithms used to create machine learning models have a highly repetitive structure. We formalize this class of circuits and propose an MPC protocol that achieves $$O(|\mathsf {C}|)$$ total complexity for this class. We implement our protocol and show that it is practical and outperforms $$O(n|\mathsf {C}|)$$ protocols for modest numbers of players.
Bilingual Cyber-aggression detection on social media using LSTM autoencoder

Cyber-aggression is an offensive behaviour attacking people based on race, ethnicity, religion, gender, sexual orientation and other traits. It has become a major issue plaguing the online social media. In this research, we have developed a deep learning-based model to identify different levels of aggression (direct, indirect and no aggression) in a social media post in a bilingual scenario. The model is an autoencoder built using the LSTM network and trained with non-aggressive comments only. Any aggressive comment (direct or indirect) will be regarded as an anomaly to the system and will be marked as Overtly (direct) or Covertly (indirect) aggressive comment depending on the reconstruction loss by the autoencoder. The validation results on the dataset from two popular social media sites: Facebook and Twitter with bilingual (English and Hindi) data outperformed the current state-of-the-art models with improvements of more than 11% on the test sets of the English dataset and more than 6% on the test sets of the Hindi dataset.
A Related work on secure event logs protection with user identity using privacy preservation for the cloud infrastructure

The cloud infrastructure is not new to the society from past one decade. But even in recent time, the companies started migrating from local services to cloud services for better connectivity and for other requirements, this is due to companies financial limitations on existing infrastructure, they are migrating to less cost and hire and fire support based cloud infrastructures. But the proposed cloud infrastructure require security on event logs accessed by different end users on the cloud environment. To adopt the security on local services to cloud service based infrastructure, it need better identify management between end users. Therefore this paper presents the related works of user identity as a service for each user involving in cloud service and the accessing permission and protection will be monitored and controlled by the cloud security infrastructures.
Design multiple: How different configurations of participation matter in design practice

This article critically interrogates how participation is practiced during the design process. We present the findings of three different configurations of participatory design workshops, each involving a different stakeholder group (age researchers, care experts and older adults). Building on insights from Science and Technology Studies (STS), we reveal how, in design practice, different configurations of participation enact and materialize multiple versions of ageing. To refer to this ontological layer of design processes, we introduce the concept “design multiple”. Our study adds to current debates on the practices of participatory design and STS, as it shows how different configurations multiply enact objects into several material realities. We raise awareness on the practices of configuring participatory design, and their ontological consequences
Bengali Sentiment Analysis of E-commerce Product Reviews using K-Nearest Neighbors

The sentiment analysis of the Bengali language is converting into a trendy research topic nowadays. Sentiment analysis is a useful technique in opinion mining, emotion extraction, and trend predictions. By sentiment analysis, the actual sentiment of a text review can be extracted. Every day, every second's people use the internet for different purposes and leave their opinions or perspective views in various places on the internet as a text format. The opinion or review on the internet can contain the author's positive, negative, and neutral views of the statement. This study proposed a machine learning-based model to predict a user's sentiment (positive, neutral, and negative) of a Bangla text review. We have applied five machine learning algorithms in our dataset, which we manually gathered from a Bangladeshi e-commerce site called “Daraz.” We have experimented with Random Forest classifier, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost algorithms with our dataset. KNN performs great among all these five algorithms in all the performance measures of accuracy, precision, recall, and f1-score. KNN shows 96.25% accuracy, 0.96 in each of precision, recall, and f1-score.
Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.
The VISIONE Video Search System: Exploiting Off-the-Shelf Text Search Engines for Large-Scale Video Retrieval

This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users’ needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.
TheiaNet: Towards fast and inexpensive CNN design choices for image dehazing

This work examines inexpensive design choices for dehazing as an end-to-end image-to-image mapping problem without relying on the physical scattering model. The proposed TheiaNet is free from intermediate-computation of transmission map, enabling haze removal in a highly resource constrained environments. The simplicity of the network is augmented by a spatial cleaning bottleneck block, that adds faster feature extraction without adding to trainable parameters. We also analyze the effectiveness of multi-cue color space (RGB, HSV, LAB, YCbCr) over single cue color space (RGB) for end-to-end dehazing. A comprehensive set of experiments were conducted on HazeRD, D-Hazy and the more recent Reside datasets. The proposed TheiaNet significantly outperforms the existing CNN and GAN based state-of-the-art methods in terms of PSNR and SSIM on all these datasets. It also outperforms all existing methods in term of speed, compute and memory efficiency, making it more efficient. This work highlights how judicious application-specific components can augment simple CNNs to denoise faster, and more accurately than deeper heavier networks, which is supported by an ablation analysis as well.
Fully Uncalibrated Image-Based Visual Servoing of 2DOFs Planar Manipulators With a Fixed Camera

We consider the uncalibrated vision-based control problem of robotic manipulators in this work. Though lots of approaches have been proposed to solve this problem, they usually require calibration (offline or online) of the camera parameters in the implementation, and the control performance may be largely affected by parameter estimation errors. In this work, we present new fully uncalibrated visual servoing approaches for position control of the 2DOFs planar manipulator with a fixed camera. In the proposed approaches, no camera calibration is required, and numerical optimization algorithms or adaptive laws for parameter estimation are not needed. One benefit of such features is that exponential convergence of the image position errors can be ensured regardless of the camera parameter uncertainties. Generally, existing uncalibrated approaches only can guarantee asymptotical convergence of the position errors. Moreover, different from most existing approaches which assume that the robot motion plane and the image plane are parallel, one of the proposed approaches allows the camera to be installed at a general pose. This also simplifies the controller implementation and improves the system design flexibility. Finally, simulation and experimental results are provided to illustrate the effectiveness of the presented fully uncalibrated visual servoing approaches.
Mutual Information Regularized Feature-level Frankenstein for Discriminative Recognition

Deep learning recognition approaches can potentially perform better if we can extract a discriminative representation that controllably separates nuisance factors. In this paper, we propose a novel approach to explicitly enforce the extracted discriminative representation d, extracted latent variation l (e,g., background, unlabeled nuisance attributes), and semantic variation label vector s (e.g., labeled expressions/pose) to be independent and complementary to each other. We can cast this problem as an adversarial game in the latent space of an auto-encoder. Specifically, with the to-be-disentangled s, we propose to equip an end-to-end conditional adversarial network with the ability to decompose an input sample into d and l. However, we argue that maximizing the cross-entropy loss of semantic variation prediction from d is not sufficient to remove the impact of s from d, and that the uniform-target and entropy regularization are necessary. A collaborative mutual information regularization framework is further proposed to avoid unstable adversarial training. It is able to minimize the differentiable mutual information between the variables to enforce independence. The proposed discriminative representation inherits the desired tolerance property guided by prior knowledge of the task. Our proposed framework achieves top performance on diverse recognition tasks, including digits classification, large-scale face recognition on LFW and IJB-A datasets, and face recognition tolerant to changes in lighting, makeup, disguise, etc.
P-OPT: Practical Optimal Cache Replacement for Graph Analytics

Graph analytics is an important workload that achieves suboptimal performance due to poor cache locality. State-of-the-art cache replacement policies fail to capture the highly dynamic and input-specific reuse patterns of graph application data. The main insight of this work is that for graph applications, the transpose of a graph succinctly represents the next references of all vertices in a graph execution; enabling an efficient emulation of Belady's MIN replacement policy. In this work, we propose P-OPT, an architecture solution that uses a specialized compressed representation of a transpose's next reference information to enable a practical implementation of Belady's MIN replacement policy. Our evaluations across multiple applications and inputs reveal that P-OPT improves cache locality for graph applications providing an average performance improvement of 33% (56% maximum) over LRU replacement.
Enhancing Security of Image Steganography Using Visual Cryptography

Steganography is the art of hiding secret data in a medium whereas visual-cryptography is a cryptographic technique where a full image is encrypted. The security of image steganography depends on the secret key set which is used for embedding the secret image. If the secret key is exposed, the secret image can be retrieved. To solve this security issue we propose a new type of steganography method using visual cryptography. In this method, text and image are used as a secret message and image for the cover object. 24-bit RGB color images are used as both secret and cover images. In this method, we use a new image namely share1 which converts a secret image to a totally different image called share2 image. Typically, knowing the extraction method, people can retrieve the secret message easily. A pseudorandomly generated image is used as a key for visual encryption and this ensures the extra layer of security. By this proposed method, we ensure more secured image steganography.
Humour support and emotive stance in comments on Korean TV drama

Viewers on viki.com comment on Korean television drama series while watching: They produce timed comments tied to the timecode of the audiovisual stream. Among the functions these comments have in the community, the expression of emotive stance is central. Importantly, this includes humour support encoded in a variety of linguistic and paralinguistic ways. Our study identifies a range of humour support indicators, which allow us to find comments that are responses to humour. Accordingly, our study explores how commenters make use of the affordances of the Viki timed comment feature to linguistically and paralinguistically encode their humorous reaction to fictional events and to previous comments. We do this both quantitatively – based on a multilingual corpus of all 320,118 timed comments that accompany five Korean dramas we randomly selected (80 episodes in total), and qualitatively based on the in-depth analysis of two episodes. What we contribute is a typology and the distribution of humour support indicators used in a novel genre of technology-mediated communication as well as insights into how the viewing community collectively does humour support. Finally, we also present the semi-automatic detection of humour support as a viable strategy to objectively identify humour-relevant scenes in Korean TV drama.