Reviewerly/exHarmony huggingface dataset:
An Efficient Ensemble of Convolutional Deep Steganalysis Based on Clustering

Steganography is the task of hiding information in some media normally images. Steganalysis is the process of discriminating such instances and clean ones. In recent years, steganalysis has tended to use deep learning for feature extraction and classification. Convolutional Neural Networks (CNN) have improved the steganalysis performance but at the cost of computational complexity and memory space due to huge amount of training data. In this paper, a new framework is proposed to reduce the learning cost by a divide and conquer strategy. In the first phase, data is divided into disjoint clusters by use of k-means. Each cluster is then fed to a separate CNN to be customized on a specific region of data space. In the final phase, the networks are merged leveraging a fast alternate-weighting process. The proposed weighting can, to some extent, compensate for reducing the size of training data per model. The experimental results show that the proposed scalable framework reduces memory and time complexity with preserving accuracy.
QCluster: Clustering Packets for Flow Scheduling

Flow scheduling is crucial in data centers, as it directly influences user experience of applications. According to different assumptions and design goals, there are four typical flow scheduling problems/solutions: SRPT, LAS, Fair Queueing, and Deadline-Aware scheduling. When implementing these solutions in commodity switches with limited number of queues, they need to set static parameters by measuring traffic in advance, while optimal parameters vary across time and space. This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. The key idea of QCluster is to cluster packets with similar weights/properties into the same queue. QCluster is implemented in Tofino switches, and can cluster packets at a speed of 3.2 Tbps. To the best of our knowledge, QCluster is the fastest clustering algorithm. Experimental results in testbed with programmable switches and ns-2 show that QCluster reduces the average flow completion time (FCT) for short flows up to 56.6%, and reduces the overall average FCT up to 21.7% over state-of-the-art. All the source code in ns-2 is available in Github [45].
Assisting Telecommunication Fraud Prediction: Detect Individuals Carrying Multiple Phones Based on Trajectory Data Mining

Telecommunication fraud grows rapidly in recent years, which brings serious property loss or even life loss to victims. However, investigation and evidence collection in such crime cases is extremely difficult, as telecommunication fraud committed by a group usually has the features of long-distance and non-contact. However, in known scenarios of telecommunications fraud, multiple mobile phones or SIM cards are essentially utilized for role pretending, identity hiding and increasing the success rate of scams. Meanwhile, these phones and cards may expose plenty of real-time location data to intentional investigators. Based on such observation, this paper gives a new way to detect telecommunications fraud by finding potential fraudsters based on trajectory data mining. Through analyzing trajectory data, individuals with multiple phones can be found and recognized as potential telecommunications fraudsters which should be intensively monitored. The trajectory clustering and FP-growth algorithm are adapted in the proposed method, and the effectiveness of the method is validated on real-world data sets and simulation data sets. The proposed method provides a technical support for the prevention of fraudulent activities.
A Survey on Recent Recommendation Systems for the Tourism Industry

Data mining is an important field that derives insights from the data and recommendation systems. Recommendation systems have become common in recent years in the field of tourism. These are widely used as a tool that can input various selection criteria and user preferences and yields travel recommendations to tourists. User's style and preferences should be constructed accurately so as to supply most relevant suggestions. Researchers proposed various types of tourism recommendation systems (TRS) in order to improve the accuracy and user satisfaction. In this chapter, the authors studied the current state of tourism recommendation system models and discussed their preference criteria. As a part of that, the authors studied various important preference factors in TRS and categorized them based on their likeness. This chapter reports TRS model future directions and compiles a comprehensive reference list to assist researchers.
Cuckoo Search Algorithm: Statistical-Based Optimization Approach and Engineering Applications

This chapter develops a statistical-based cuckoo search algorithm (SCSA) for its engineering applications. A statistical technique is embedded into the cuckoo search algorithm so as to enhance the computational efficiency. A multi-objective optimization problem for a camera positioning device is considered as an application case of the SCSA. The pseudo-rigid-body model, an analytical approach, is used to establish the objective functions, including the displacement and natural resonant frequency of the device. The optimization problem for the device is formed, including design variables, objective functions, and constraints. The Taguchi method is then employed to build a set of experimental matrix so as to retrieve the outcome quality characteristics. The signal-to-noise ratios and the analysis of variance are utilized so as to determine a new constraint range for variables. Based on the results of statistical analyses, a new initial population range for the SCSA is redetermined. This refinement helps to reduce the computational time, human work, and cost. The optimal result is found by using the proposed SCSA approach. A prototype is made so as to verify the estimated results. The SCSA approach is compared with other metaheuristic algorithms. The results are proven that the convergent speed of the proposed SCSA is better than other algorithms such as GA, DE, AEDE, PSO, and PSOGSA. The proposed statistical-based cuckoo search algorithm is expected to solve complex optimization problems with multiple constraints and multiple variables.
Multi-Episodic Perceived Quality of an Audio-on-Demand Service

Quality of Experience is traditionally evaluated by using short stimuli usually representing parts or single usage episodes. This opens the question on how the overall service perception involving multiple usage episodes can be evaluated—a question of high practical relevance to service operators. Despite initial research on this challenging aspect of multi-episodic perceived quality, the question of the underlying quality formation processes and its factors are still to be discovered. We present a multi-episodic experiment of an Audio-on-Demand service over a usage period of 6 days with 93 participants. Our work directly extends prior work investigating the impact of time between usage episodes. The results show similar effects — although the recency effect is not statistically significant. In addition, we extend prediction of multi-episodic judgments by accounting for the observed saturation.
Space Efficient Construction of Lyndon Arrays in Linear Time

Given a string S of length n, its Lyndon array identifies for each suffix S[i..n] the next lexicographically smaller suffix S[j..n], i.e. the minimal index j > i with S[i..n] ≻ S[j..n]. Apart from its plain (n log₂ n)-bit array representation, the Lyndon array can also be encoded as a succinct parentheses sequence that requires only 2n bits of space. While linear time construction algorithms for both representations exist, it has previously been unknown if the same time bound can be achieved with less than Ω(n lg n) bits of additional working space. We show that, in fact, o(n) additional bits are sufficient to compute the succinct 2n-bit version of the Lyndon array in linear time. For the plain (n log₂ n)-bit version, we only need 𝒪(1) additional words to achieve linear time. Our space efficient construction algorithm makes the Lyndon array more accessible as a fundamental data structure in applications like full-text indexing.
Classification of Viewing Abandonment Reasons for Adaptive Bitrate Streaming

As adaptive bitrate streaming services have spread, it has become more important for video streaming providers to control video quality and prevent viewing abandonments. However, since viewing abandonments are caused not only by quality degradations but also by a lack of users' interest in contents, it will first be necessary to clarify how quality and/or content affect viewing abandonments. To investigate this, we conducted an adaptive bitrate streaming experiment and developed a viewing-abandonment-reason-classification model that classifies abandonment reasons into quality or content. Using training data, we developed four models (logistic regression, classification tree, random forests, and support vector machine) where feature variables related to application quality, users' operation behaviors, and the attributes of viewed contents were used as explanatory variables. These four models were validated by using validation data. From the results, the support vector machine model was considered to be the best since it obtained relatively good validation results and did not appear to be over-trained.
Artificial intelligence methods for predictive image-based grading of human cancers

Surgical pathology and the practice of general medicine are often described as having legitimate components of art as well as science. That perception is perhaps less relevant in the current information age with the adoption of genomic medicine, in large part due to the growth in disease-specific causal knowledge. As a surgical pathologist, gestalt and intuition are words you may not use to describe how you arrive at a specific patient's diagnosis, but it is certainly understood that with experience, in particular with a deep immersion in a subspecialty, comes an understanding of histopathology that cannot easily be transmitted or formalized. This is not in any way to delegitimize pathology or relegate it to the unscientific, rather to illustrate a strength of the human brain. For well over 100 years using a microscope, pathologists have been applying this strength to group and separate disease entities into diagnoses and their subtypes. The brain seems to be good at pattern recognition, filtering noise, and dealing with variability, and from a histopathology perspective, this has complemented the medical need quite well for over a century. Although the concept of tumor differentiation and its relationship to clinical outcome dates back to the early days of histopathology until fairly recently, limited treatment options diminished the necessity for more precise and standardized grading. The following chapter explores how the growing fields of computational pathology, machine learning, and deep learning are coming together to address the need for more precise and standardized grading. Starting with tissue preparation and image acquisition, the discussion roughly follows the steps involved in developing an image analysis–based multivariate grading system with attention drawn in each subsection to inherent obstacles, published state-of-the-art techniques, and the authors' own experience with solutions.
Malicious changeload for the resilience evaluation of self-adaptive authorisation infrastructures

Self-adaptive systems are able to modify their behaviour and/or structure in response to changes that occur to the system, its environment, or even its goals. In terms of authorisation infrastructures, self-adaptation has shown to be a promising solution for enforcing access control policies and subject access privileges when mitigating insider threat. This paper describes the resilience evaluation of a self-adaptive authorisation infrastructure by simulating a case study related to insider threats. As part of this evaluation, a malicious changeload has been formally defined in order to describe scenarios of abuse in access control. This malicious changeload was then used to stimulate self-adaptation within a federated authorisation infrastructure. The evaluation confirmed the resilience of a self-adaptive authorisation infrastructure in handling abuse of access under repeatable conditions by consistently mitigating abuse under normal and high loads. The evaluation has also shown that self-adaptation had a minimal impact on the authorisation infrastructure, even when adapting authorisation policies while mitigating abuse of access.
Comparative Study of Optimal Long Short Term Memory Networks for One Day Ahead Solar Irradiance Hourly Forecast

Energy sustenance is one of the key challenges India is facing in the contemporary time. Rise in global warming and the increasing need for dependency on clean energy has motivated researchers to develop novel techniques for harnessing maximum energy from renewable sources such as solar irradiance. However, one major issue which is impeding the large scale optimal implementation of solar farms is the uncertainty associated with solar irradiance. Although several statistical forecasting methods have helped in this regard, they could not contribute to efficient utilization of solar energy. In this work, long short term memory networks (LSTMs) are implemented for modelling the time series data of solar irradiance. LSTMs are deep neural networks which are proven to be extremely efficient in modelling nonlinear time series data with long term dependencies. However, LSTM networks are modelled using several heuristically governed parameters, making them an ineffective tool for time series regression. A novel multi-objective evolutionary optimization framework is proposed for optimal design of LSTM networks for emulating the real world solar irradiance data. The optimally trained LSTMs are used to forecast 1 day-ahead hourly prediction. LSTMs are compared with state-of-the-art system identification tools - Wavelet networks and feedforward neural networks through nonlinear auto-regressive exogenous modelling. LSTMs were found to be better with a root mean square error of 13% and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (correlation coefficient-a statistical measure of goodness of fit) value of 0.976.
Revisiting Old Combinatorial Beasts in the Quantum Age: Quantum Annealing Versus Maximal Matching

This paper experimentally investigates the behavior of analog quantum computers such as commercialized by D-Wave when confronted to instances of the maximum cardinality matching problem specifically designed to be hard to solve by means of simulated annealing. We benchmark a D-Wave "Washington" (2X) with 1098 operational qubits on various sizes of such instances and observe that for all but the most trivially small of these it fails to obtain an optimal solution. Thus, our results suggests that quantum annealing, at least as implemented in a D-Wave device, falls in the same pitfalls as simulated annealing and therefore suggest that there exist polynomial-time problems that such a machine cannot solve efficiently to optimality.
gRemote: API-Forwarding Powered Cloud Rendering

Traditional GPU resource allocation approaches, widely adopted in today's data centers, only focus on the server-side functions while ignoring the client-side. These approaches waste client-side hardware resources. To solve this problem, remote API-forwarding architectures appear. Through running applications on the client-side, remote API-forwarding architectures offload some workloads to the client. However, many remote API-forwarding systems suffer from one big issue: shared-resource interference, stemming from two reasons: (a) GPU resource racing caused by resource overuse for a single client, and (b) CPU resource racing caused by resource shortage among clients. This paper presents gRemote, an open-source GPU-remoting system that can address this issue. To mitigate the CPU resource shortage, gRemote improves CPU configurations by expanding CPU resources from the server-side to both server- and client-side. To maintain the reasonable GPU usage for individual tasks, we innovate a new resource-sharing mechanism called GPU throttle. gRemote supports 1,228 OpenGL commands with around 10% shared-resource interference.
Recurrent Knowledge Attention Network For Movie Recommendation

A primary concern of the current recommendation system is how to provide personalized recommendation to users and improve the accuracy and user satisfaction. The Knowledge Graph(KG) provides a new way to improve the recommendation system. This paper proposes a movie recommendation model based on Recurrent Neural Network(RNN) and KG─RKAN, which uses the auxiliary information in the KG to look for the potential interests of users for personalized recommendations. In addition, in order to solve the problem of user’s individual interests, an attention module was designed in RKAN,using different weights to converge user’s interest; multiple sets of negative samples were used for comparison to balance model training; data collected from the real movie data set Movielens and IMDB was mapped into a new data set for testing. Experiments show that the model has significantly improved the recommendation accuracy, and can better explain the reasons behind recommendations.
Consonance, Dissonance, and Formal Proportions in Two Works by Sofia Gubaidulina

Consonance and dissonance are frequently invoked in discussions of Gubaidulina’s music, particularly in terms of the composer’s intentional contrasts between timbres and her golden section-oriented formal planning. In this paper, I use the notion of dissonance to mediate between several competing theories of Gubaidulina’s music and theories of timbre more broadly. Certain passages in her Meditation on the Bach Chorale “Vor deinen Thron tret’ ich hiermit” (1993) are identified as dissonant based on her compositional theories and on sketch material from the Paul Sacher Stiftung, and on further examples drawn from her Am Rande des Abgrunds (2002). Broadly speaking, “dissonant” sounds are closer to white noise than “consonant” sounds, aligning with empirical studies of dissonance perception as well as with spectralist compositional theories. However, Gubaidulina’s music uses this extended notion of dissonance particularly musically; dissonances have specific structural roles in a work and resolve to consonances.
Prawf: An Interactive Proof System for Program Extraction

We present an interactive proof system dedicated to program extraction from proofs. In a previous paper [5] the underlying theory IFP (Intuitionistic Fixed Point Logic) was presented and its soundness proven. The present contribution describes a prototype implementation and explains its use through several case studies. The system benefits from an improvement of the theory which makes it possible to extract programs from proofs using unrestricted strictly positive inductive and coinductive definitions, thus removing the previous admissibility restrictions.
Visual Analytics Methods for Eye Tracking Data

Nowadays, eye tracking data have become important and valuable information that help to understand the behavior of users. The gathering of these data is not an issue anymore. However, the problem is the analysis process and especially how can these raw data be converted to understandable and useful information. Visual analytics can solve this issue by combining human analytics skills and the advanced computer analytics. This leads to the novel discoveries and helps humans take control of the analytical process. These visualizations can be used to solve difficult problems by discovering new unknown patterns of available data. In this work, we discussed different methods that are used in the case of eye tracking data, and we addressed the challenges of visual analytics in this context.
An Extension of the Apriori Algorithm for Finding Frequent Items

The main purpose of data mining is to discover hidden and valuable knowledge from data. The Apriori algorithm is inefficient due to bulky deals of searching in a dataset. Bearing this in mind, this paper proposes an improved algorithm from Apriori using an intelligent method. Proposing an intelligent method in this study is to fulfill two purposes: First, we demonstrated that to create itemsets, instead of adding one item at each step, several items could be added. With this operation, the number of k-itemset steps will decline. Secondly, we have proved that by storing the transaction number of each itemset, there would be a diminishment in the time required for the dataset searches to find the frequent k-itemset in each step. To evaluate the performance, the Intelligent Apriori (lAP) algorithm has been compared with the MDC algorithm. The results of this experiment exhibit that since the transaction scans used to obtain the itemset momentously reduced in number, there was a considerable fall in the runtime needed to obtain a frequent itemset by the proposed algorithm. In this study, the time required to generate frequent items had a 46% reduction compared to that of the MDC_ Apriori algorithm.
A Gated Recurrent Unit Network Model for Predicting Open Channel Flow in Coal Mines Based on Attention Mechanisms

The prediction of water inflow during coal mining is an important issue. There are many factors that can affect the water inflow in mines. The intercoupling of these factors makes it difficult for current water inflow forecasting methods to meet the needs of real-time forecasting. Open channels are the main devices used for mine drainage, and their flow rate reflects the water inrush of a mine to some extent. This paper uses a hybrid neural network model combining attention mechanisms and a gated recurrent unit network to make real-time predictions of open channel flow. First, attention mechanisms are used to learn the interdependence between multisource hydrosensor data, and then, a gated recurrent unit network is employed to capture the dependencies on different time scales to improve the prediction accuracy of the neural network model. Finally, we design a series of comparative experiments to verify and analyse the performance of the hybrid neural network model. The experimental verification shows that the proposed model can learn the dependency relationships among multisource sensors, and the modelling of these dependencies can greatly improve the prediction accuracy of real-time flow in open channels.
Techniques for Raising the Remainder to a Power in the System of Residual Classes

The article discusses computer calculations implemented in the system of residual classes. These techniques make it possible to implement fast and reliable arithmetic operations, with the possibility of parallelizing computations. We propose a method of raising the raise integer residues to an arbitrary power of a natural number according to an arbitrary module represented by a system of residue classes. The proposed method is rest on a tabular principle of the implementation of arithmetic operations using the code for information compression of tabular data. The novelty of the material of the article lies in the fact that when implementing the method of raising the remainder of integers by an arbitrary modulus to the power of a natural number, the remainders of the numbers are presented in artificial form. The tabular principle ensures the maximum speed realization of operation of raising the remainder of integers realized in the SRC.