Reviewerly/exHarmony huggingface dataset:
A game-theoretic approach for cost-aware load balancing in distributed systems

In this paper, we consider a load balancing problem in distributed systems, that has two conflicting objectives: (i) minimizing the users’ expected response time and (ii) minimizing the total monetary cost incurred by each user. To satisfy both the objectives simultaneously, we consider the objectives in an integrated manner and formulate the problem as an optimization problem. We then cast it into a game-theoretic setting and model the load balancing problem as a non-cooperative game. To solve the game, we characterize the best response strategy for each player, and derive a decentralized algorithm called Cost-Aware Load Balancing Algorithm (CALBA). We conduct a rigorous experimental study to demonstrate the effectiveness of CALBA. Further, to establish the effectiveness of CALBA, we compare it with three other load balancing schemes, i.e., MinRT, MinCost, and GPMS, using various system configurations such as varying system size, varying system utilization, and system heterogeneity, across multiple performance indicators. The computational results show that textitCALBA outperforms the competitive schemes by reducing the response time and cost, and unlike others, CALBA produces an allocation of load which guarantees fairness (in terms of response time) between the users. In a nutshell, the results demonstrate the suitability of CALBA in realistic scenarios as it is an adaptable and feasible approach to get a cost-aware load balancing solution.
Detecting Malign Encrypted Network Traffic Using Perlin Noise and Convolutional Neural Network

Machine learning supports analysis of traffic packets by featuring the payloads, increasing the chances of detecting new variants of malware. However, adversaries take advantage of current cryptographically protected network communication to hide the payload features and as a result, avoid detection. In this research, we propose a new method enhancing generalization of Convolutional Neural Networks model to detect malicious encrypted network traffic. Since the payload is encrypted, we extract contextual features from the connection meta-data that best characterizes the behavior of traffics. Our proposed approach encodes given connection features into images using Perlin noise to train the deep learning model for binary classification of connection flows. We applied the model to captured real botnet traffic dataset mixed with normal and background traffic, and obtained a high accuracy of 97% detection and low false negative rate of 0.4%.
Comparing various solar irradiance categorization methods – A critique on robustness

Traditional ways of planning and operation of electricity networks have been challenged lately by the spread of variable renewable energy sources, especially solar photovoltaics, and the need for better forecasting has increased interest in various solutions. Categorization of solar irradiance data, as one of the earliest applied techniques, is a frequently discussed topic in the literature, but the efficiency of different methods may be significantly variable. The aim of this paper is to compare various categorization methods using a one-year-long solar irradiance dataset and reflect on their inefficiencies and the need for more timely solutions. Six methods have been selected and implemented, including deterministic and non-deterministic ones. The number of groups created by the methods varies between three and five, and they also use data with different temporal resolution. The aim of the comparison was to reveal the strengths and weaknesses of the implemented methods and to highlight possible contradictions among them, based on two tests: uniformity in identifying days with clear or cloudy conditions and contradictory identifications. The results have shown that the usability of certain methods is limited as they are very sensitive to input data, and categorization is often inconsistent, which limits the usability and dissuades users of such methods.
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition

Fine-grained action recognition datasets exhibit environmental bias, where even the largest datasets contain sequences from a limited number of environments due to the challenges of large-scale data collection. We show that multi-modal action recognition models suffer with changes in environment, due to the differing levels of robustness of each modality. Inspired by successes in adversarial training for unsupervised domain adaptation, we propose a multi-modal approach for adapting action recognition models to novel environments. We employ late fusion of the two modalities commonly used in action recognition (RGB and Flow), with multiple domain discriminators, so alignment of modalities is jointly optimised with recognition. We test our approach on EPIC Kitchens, proposing the first benchmark for domain adaptation of fine-grained actions. Our multi-modal method outperforms single-modality alignment as well as other alignment methods by up to 3%.
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization

Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context. This is intuitively useful for generalization, especially in Named-Entity Recognition where it is crucial to detect mentions never seen during training. However, standard English benchmarks overestimate the importance of lexical over contextual features because of an unrealistic lexical overlap between train and test mentions. In this paper, we perform an empirical analysis of the generalization capabilities of state-of-the-art contextualized embeddings by separating mentions by novelty and with out-of-domain evaluation. We show that they are particularly beneficial for unseen mentions detection, especially out-of-domain. For models trained on CoNLL03, language model contextualization leads to a +1.2% maximal relative micro-F1 score increase in-domain against +13% out-of-domain on the WNUT dataset (The code is available at https://github.com/btaille/contener ).
The role of data-reuse experience in biological scientists’ data sharing: an empirical analysis

Purpose The purpose of this paper is to investigate how scientists’ prior data-reuse experience affects their data-sharing intention by updating diverse attitudinal, control and normative beliefs about data sharing. Design/methodology/approach This paper used a survey method and the research model was evaluated by applying structural equation modelling to 476 survey responses from biological scientists in the USA. Findings The results show that prior data-reuse experience significantly increases the perceived community and career benefits and subjective norms of data sharing and significantly decreases the perceived risk and effort involved in data sharing. The perceived community benefits and subjective norms of data sharing positively influence scientists’ data-sharing intention, whereas the perceived risk and effort negatively influence scientists’ data-sharing intention. Research limitations/implications Based on the theory of planned behaviour, the research model was developed by connecting scientists’ prior data-reuse experience and data-sharing intention mediated through diverse attitudinal, control and normative perceptions of data sharing. Practical implications This research suggests that to facilitate scientists’ data-sharing behaviours, data reuse needs to be encouraged. Data sharing and reuse are interconnected, so scientists’ data sharing can be better promoted by providing them with data-reuse experience. Originality/value This is one of the initial studies examining the relationship between data-reuse experience and data-sharing behaviour, and it considered the following mediating factors: perceived community benefit, career benefit, career risk, effort and subjective norm of data sharing. This research provides an advanced investigation of data-sharing behaviour in the relationship with data-reuse experience and suggests significant implications for fostering data-sharing behaviour.
Dark Web Along With The Dark Web Marketing And Surveillance

Cybercrimes and cyber criminals widely use dark web and illegal functionalities of the dark web towards the world crisis. More than half of the criminal activities and the terror activities conducted through the dark web such as, cryptocurrency, selling human organs, red rooms, child pornography, arm deals, drug deals, hire assassins and hackers, hacking software and malware programs, etc. The law enforcement agencies such as FBI, NSA, Interpol, Mossad, FSB etc, are always conducting surveillance programs through the dark web to trace down the mass criminals and terrorists while stopping the crimes and the terror activities. This paper is about the dark web marketing and surveillance programs. In the deep end research will discuss the dark web access with securely and how the law enforcement agencies exponentially tracking down the users with terror behaviours and activities. Moreover, the paper discusses dark web sites which users can grab the dark web jihadist services and anonymous markets including safety precautions.
Improving the Quality of Management with the Concept of Decision Support Systems in Determining Factors for Choosing a Cafe based on Consumers

Abstract The management quality of a cafe really needs to be maintained. This is considering that Cafe is one of the fast-growing and promising businesses. This research was conducted in the city of Pematangsiantar. The development of cafes in Pematangsiantar has not been accompanied by developments in modern technology. This makes customers still feel confused and need information to decide to visit a cafe that is in accordance with their wishes. The purpose of this research is to analyze the Analytical Hierarchy Process (AHP) method in choosing the dominant factor in determining the best cafe factor based on consumers from a number of assessment criteria. Researchers used 8 assessment criteria including: (C1) reliability, (C2) responses, (C3) tangible, (C4) certainty, (C5) security, (C6) ease, (C7) credibility, and (C8) understanding needs. The questionnaire assessment was carried out using 7 alternative cafes among 339, A +, Kok Tong, Coffee Cups, Coffee Time, Eldivo and Indis Cafe. The results of the questionnaire were processed and calculated using the Analytical Hierarchy Process (AHP) method. The results of the study showed that (C1) reliability with vector eigenvalues (0.231) as the first, second and third rank (C6) Ease with vector eigenvalue (0.141) and (C2) responses with vector eigenvalue (0.130). It is hoped that this research can provide input to the owner in creating a decent cafe atmosphere to be a family gathering place..
Automatic Boolean Query Formulation for Systematic Review Literature Search

Formulating Boolean queries for systematic review literature search is a challenging task. Commonly, queries are formulated by information specialists using the protocol specified in the review and interactions with the research team. Information specialists have in-depth experience on how to formulate queries in this domain, but may not have in-depth knowledge about the reviews' topics. Query formulation requires a significant amount of time and effort, and is performed interactively; specialists repeatedly formulate queries, attempt to validate their results, and reformulate specific Boolean clauses. In this paper, we investigate the possibility of automatically formulating a Boolean query from the systematic review protocol. We propose a novel five-step approach to automatic query formulation, specific to Boolean queries in this domain, which approximates the process by which information specialists formulate queries. In this process, we use syntax parsing to derive the logical structure of high-level concepts in a query, automatically extract and map concepts to entities in order to perform entity expansion, and finally apply post-processing operations (such as stemming and search filters).
Weakly Supervised Attention for Hashtag Recommendation using Graph Data

Personalized hashtag recommendation for users could substantially promote user engagement in microblogging websites; users can discover microblogs aligned with their interests. However, user profiling on microblogging websites is challenging because most users tend not to generate content. Our core idea is to build a graph-based profile of users and incorporate it into hashtag recommendation. Indeed, user’s followee/follower links implicitly indicate their interests. Considering that microblogging networks are scale-free networks, to maintain the efficiency and effectiveness of the model, rather than analyzing the entire network, we model users based on their links towards hub nodes. That is, hashtags and hub nodes are projected into a shared latent space. To predict the relevance of a user to a hashtag, a projection of the user is built by aggregating the embeddings of her hub neighbors guided by an attention model and then compared with the hashtag. Classically, attention models can be trained in an end to end manner. However, due to the high complexity of our problem, we propose a novel weak supervision model for the attention component, which significantly improves the effectiveness of the model. We performed extensive experiments on two datasets collected from Twitter and Weibo, and the results confirm that our method substantially outperforms the baselines.
Deep Global and Local Generative Model for Recommendation

Deep generative model, especially variational auto-encoder (VAE), has been successfully employed by more and more recommendation systems. The reason is that it combines the flexibility of probabilistic generative model with the powerful non-linear feature representation ability of deep neural networks. The existing VAE-based recommendation models are usually proposed under global assumption by incorporating simple priors, e.g., a single Gaussian, to regularize the latent variables. This strategy, however, is ineffective when the user is simultaneously interested in different kinds of items, i.e., the user’s preference may be highly diverse. In this paper, thus, we propose a Deep Global and Local Generative Model for recommendation to consider both local and global structure among users (DGLGM) under the Wasserstein auto-encoder framework. Besides keeping the global structure like the existing model, DGLGM adopts a non-parametric Mixture Gaussian distribution with several components to capture the diversity of the users’ preferences. Each component is corresponding to one local structure and its optimal size can be determined via the automatic relevance determination technique. These two parts can be seamlessly integrated and enhance each other. The proposed DGLGM can be efficiently inferred by minimizing its penalized upper bound with the aid of local variational optimization technique. Meanwhile, we theoretically analyze its generalization error bounds to guarantee its performance in sparse feedback data with diversity. By comparing with the state-of-the-art methods, the experimental results demonstrate that DGLGM consistently benefits the recommendation system in top-N recommendation task.
An End-to-end Topic-Enhanced Self-Attention Network for Social Emotion Classification

Social emotion classification is to predict the distribution of different emotions evoked by an article among its readers. Prior studies have shown that document semantic and topical features can help improve classification performance. However, how to effectively extract and jointly exploit such features have not been well researched. In this paper, we propose an end-to-end topic-enhanced self-attention network (TESAN) that jointly encodes document semantics and extracts document topics. In particular, TESAN first constructs a neural topic model to learn topical information and generates a topic embedding for a document. We then propose a topic-enhanced self-attention mechanism to encode semantic and topical information into a document vector. Finally, a fusion gate is used to compose the document representation for emotion classification by integrating the document vector and the topic embedding. The entire TESAN is trained in an end-to-end manner. Experimental results on three public datasets reveal that TESAN outperforms the state-of-the-art schemes in terms of higher classification accuracy and higher average Pearson correlation coefficient. Furthermore, the TESAN is computation efficient and can generate more coherent topics.
Variationally scheduled quantum simulation

Eigenstate preparation is ubiquitous in quantum computing, and a standard approach for generating the lowest-energy states of a given system is by employing adiabatic state preparation (ASP). In the present work, we investigate a variational method for determining the optimal scheduling procedure within the context of ASP. In the absence of quantum error correction, running a quantum device for any meaningful amount of time causes a system to become susceptible to the loss of relevant information. Therefore, if accurate quantum states are to be successfully generated, it is crucial to find techniques that shorten the time of individual runs during iterations of annealing. We demonstrate our variational method toward this end by investigating the hydrogen and P4 molecules, as well as the Ising model problem on a two-dimensional triangular lattice. In both cases, the time required for one iteration to produce accurate results is reduced by several orders of magnitude in comparison to what is achievable via standard ASP. As a result, the required quantum coherence time to perform such a calculation on a quantum device becomes much less stringent with the implementation of this algorithm. In addition, our variational method is found to exhibit resilience against control errors, which are commonly encountered within the realm of quantum computing.
Bloxy: Providing Transparent and Generic BFT-Based Ordering Services for Blockchains

With the wide-spread use of blockchain technology, Byzantine fault-tolerant (BFT) protocols are explored as a means to achieve consensus on which transactions should be processed next. BFT protocols are not a one-size-fits-all solution: they should be chosen according to the blockchain's use case, which can range from supply chain management to decentralised storage, requiring specialisation e.g. regarding throughput, latency, or level of decentralisation. Previously, consensus protocols were usually hardcoded into the blockchain infrastructure and could not be exchanged, therefore inhibiting flexible use of an otherwise generic blockchain infrastructure. Hyperledger Fabric claims to provide modular consensus and support for crash-fault and Byzantine fault tolerant protocols. However, integrating a BFT protocol has shown that Fabric's architecture is currently not well-suited for this fault model as it requires substantial changes and thereby breaks Fabric's modularity. This also has to be repeated for each integrated BFT protocol. In this paper, we present Bloxy, a blockchain-aware trusted proxy running on the replica that encapsulates all BFT client functionality. Bloxy enables transparent access to generic BFT frameworks and preserves Fabric's modularity even for the Byzantine fault model. It runs inside a trusted execution environment based on Intel's Software Guard Extensions. Bloxy offers blockchain-specific communication mechanisms as well as short-term block storage to handle crashes or disconnects to ensure that all nodes receive block updates. We implemented two Bloxy-based ordering services based on PBFT and the hybrid BFT protocol Hybster. Our evaluation shows that our approach increases throughput by up to 71% compared to directly integrated BFT protocols.
An Analysis Report on Green Cloud Computing Current Trends and Future Research Challenges

Today cloud computing became an impressive solution to address the challenges in storage and process of high volume data, with low-cost, high-speed, on-demand and pay-per-use characteristics. Although rapid progression has been recorded in the area of cloud computing and its services, attaining the implementation of green clouds is still under development due to lack of research and several barriers in its implementation. Green clouds are committed to design as eco-friendly, energy efficient, max resource utilizable, low carbon emissions, long lasting and recyclable. In order to satisfy the ever growing enterprise data storage and processing needs, the cloud service providers are coming up with cutting edge technologies like Green Cloud Computing in cloud architecture design to reduce, the huge power consumption, water consumption, need of physical hardware peripherals, infrastructure and harmful carbon emissions etc. To protect our environment from cloud negative impacts, the service providers must adopt and update their cloud infrastructure towards green computing. Green computing researches widely focus on designing of efficient clouds with green characteristics like Power Management, Virtualization, High Performance Computing, Load balancing, Green data center, Reusability, Recyclability etc. As part of my research on green clouds, this paper presents an analysis report about the green cloud computing and its characteristics in detailed manner. This paper thoroughly discusses about the former green computing achievements, current trending concepts of green computing and future research challenges as well. This comprehensive green cloud analysis report helps the naïve green research fellows to learn about green cloud topics and to understand the green cloud future research challenges.
A Fuzzy Modal Logic for Fuzzy Transition Systems

This paper intends to contribute with a new fuzzy modal logic to model and reason about transition systems involving uncertainty in behaviours. Our formalism supports fuzziness at transitions and on the proposition symbols assignment levels. Against of other approaches in the literature, our bisimulation and bisimilarity notions generalise the analogous standard notions of classic modal logic and of process algebras. Moreover, the outcome of our logic is also fuzzy, with the semantic interpretation of connectives supported by the Gödel algebra.
Persian Accents Identification Using Modeling of Speech Articulatory Features

One of the most commonly used areas of speech processing is the accent recognition system, that recognizes a speaker’s accent from speaking it. In this study, using hierarchical neural networks, speech articulatory features in Persian speech such as "Attribute Manner" and "Attribute Place "are appropriately modeled and extracted. Subsequently, using native modelers such as GMM_UBM and I_vector, 5 native accents were identified in FarsDat data. In this study, it has been shown that speech articulatory features usually perform better in distinguishing and identifying native Persian accents than spectral features such as MFCC and SDC. The results show that by applying a dimensionality reduction such as PCA to the Attribute Manner of phoneme of the long-term speech articulatory features, the average error rate is 8.37% and the average accent detection cost is 6.68. Compared to conventional features like MFCC + SDC, they can reduce relative error rates and average error costs by 45.12% and 29.28%, respectively. In addition, the best average accuracy for the 5 native accents is 75.29%. These features also exhibit better resistance to conventional spectral features such as MFCC and SDC when there is a shortage of educational data.