Reviewerly/exHarmony huggingface dataset:
MDRAA: A Multi-decisive Resource Allocation Approach to Enhance Energy Efficiency in a Cloud Computing Environment

With the development of cloud environment which is serving user requests, storing data etc., energy consumption has become a big issue. Increased energy data consumption of data centers emit a large amount of CO2 and also has made the IT industry to worry about when we think of green computing. As more tasks are running in the datacenter, minimizing the energy consumption becomes a challenge. Technologies like virtualization, migration, and DVFS (Dynamic Voltage and Frequency Scaling) and workload consolidation are the appreciating solutions and hence used in our work to reduce energy consumption and power without affecting the progress rate of jobs. Virtualization is a technology in which physical machines are partitioned into multiple virtual machines (VMs). Techniques like Fuzzy logic and Linear Regression are also used for the host discovery and allocation of VM identified for migration. We have also compared our proposed mechanism with existing systems in various dimensions. To understand this, a prior knowledge of cloud’s energy consumption is required.
Severe Cold Weather is Valuable to Build Northeast China as the Largest Data and Computing Centre in China and in the World

Revitalization of economy in Northeast China is very important for sustainable development in Chinese economy because the economy in Northeast China lags behind most parts of China. An important factor, which impedes the economic development in Northeast China, is its severe cold weather. Consequently, the Northeast China faces a series of issues, for example, less attraction to youth, decline of population, aging of population, lack of investment, out-of-date infrastructure, etc. In this study, we propose to restructure the economy in Northeast China from the labour-intensive heavy industry to the high-tech industry based on data storage and computing. This is possible because the severe cold weather is good for cooling data storage and computing facility, which is highly energy-demanded. Taking this economic advantage, it could transform the Northeast China into the largest data storage and computing centre in China, which would promote the development of green economy in China, and even in the world.
Towards high-level fuzzy control specifications for building automation systems

The control logic underlying building automation systems has consisted, traditionally, of embedded discrete programs created using either low-level or proprietary scripting languages, or using general purpose fourth-generation visual languages like Simulink. It is also well known that programs developed in this way are hard to evolve, test, and maintain. These difficulties are intensified when continuous control problems have to be tackled or when the actuation must vary continually subject to the sensor inputs. Such is the case in day-lighting or occupancy-based control applications. In this paper, we propose a declarative high-level Domain-Specific Language that aims to reduce the effort required to specify the control logic of building automation systems. Our language combines fuzzy logic and temporal logic, enabling to define the behaviour in terms of domain abstractions. Finally, the approach has been validated in two ways: (i) in a case study that simulates the control system of an automated office room and (ii) by means of an empirical study to confirm usability (with a System Usability Scale questionnaire) and effectiveness, here regarded from the perspective of correctness, of the proposed language with respect to a well-known language like Simulink.
Inverse Classification for Military Decision Support Systems

We propose in thisRusso, Pietro paper a military application, which can be used in civilPanella, Massimo contexts as well, for solving inverse classification problems. Pattern recognition and decision support systems are typical tools through which inverse classification problems can be solved in order to achieve the desired goals. As standard classifiers do not work properly for inverse classification, which is an inherent ill-posed problem and therefore difficult to be inverted, we propose a new approach that exploits all the information associated with the decisions observed in the past. The experimental results prove the feasibility of the proposed algorithm, with errors lower than 10% with respect to standard classification models.
CAPTCHA and Symbiotic Interaction

This chapter describes the CAPTCHA interfaces under the usability perspective, which is related to the interaction of the humans to the test. In particular, this interaction is connected to the efficiency aspect of the usability. This concerns the analysis of the solving time, also called response time, of different types of CAPTCHA by different users with specific personal and demographic characteristics. The dependence of the response time to CAPTCHA on the different user’s characteristics is explored by statistical analysis with the formulation of meaningful hypotheses which should be (dis)proved. Then, more complex patterns describing the dependence of the response time on the co-occurrence of different user’s characteristics are extracted by employing advanced statistical analysis. This is accomplished by using association rule mining on the collected users’ data.
The Incremental Artificial Immune System for Arabic Handwritten Recognition

The historical document is a treasure. The frequent use of these documents requires having a numeric copy. The use of these numeric documents requires developing techniques to facilitate their use. The search by content, the word spotting, and handwriting recognition became important points of research in document analysis. For this purpose, in this article is covered the recognition of the Arabic manuscript names extracted from the register of names of the Tunisian national archive. In the study, the authors have used several techniques for extracting knowledge, coding, and name recognition. The authors have also optimized the clonclas algorithm using the incremental principle from the i2gng algorithm. The results encourage continuing exploration.
Enhanced Document Retrieval and Discovery Based on a Combination of Implicit and Explicit Document Relationships

With the rapid increase of digital information we are dealing with in our daily work, we face significant document retrieval and discovery challenges. We present a novel document retrieval and discovery framework that addresses some of the limitations of existing solutions. An innovative aspect of our solution is the combination of implicit and explicit links between documents in the retrieval as well as in the visualisation process, in order to improve document retrieval and discovery. Our framework exploits implicit relationships between documents--defined by the similarity of their content as well as their metadata--and explicit links (hyperlinks) defined between documents based on a third-party link service. Further, the software framework can be extended with arbitrary third-party visualisations. Last but not least, our search query interface offers advanced features not available in most existing document retrieval systems.
An Unsupervised Keywords Extraction Approach for Chinese Government Documents

In China, government documents play an important role in making formal regulation, government policy, etc. The number of government official documents is increasing by hundreds of thousands every year. The huge quantity and diversified content make it difficult to accurately search and classify government documents. With accurate keywords of documents, the accuracy of searching and classification can be improved. Generally, the keywords extraction methods can be classified into two categories: supervised learning and unsupervised learning. Due to the lack of labelling data, this paper focuses on unsupervised methods. Based on the characteristics of official documents, this paper proposes a method for automatic keyword extraction based on a method which is a weighted sum of TF-IDF and TextRank. This method can dynamically adjust the weighting coefficients according to the genre and writing features of official documents so that it could take advantages of each of the keyword extraction method. Experimental results show that this method is superior to the traditional unsupervised keyword automatic extraction method.
An Inquiry into Localized Email Spam

Although email spam has been decreasing in recent years, it is still a significant problem. Unsolicited messages are not only an annoying waste of time, but may also lead users to dubious websites where their personal information is collected or malicious code is executed. In this paper, we study a relatively new trend in the world of spamming, localization of spam email. It seems spammers are now targeting people in smaller countries, such as Finland, by sending junk mail in their native languages. As a case study, we analyze a set of over 300 spam email messages written in Finnish and study how localization of messages is used to convince the victims to fall into spammers' traps. Our results indicate that localized and targeted spam is a significant threat which needs to be better addressed when planning security measures and educating the public.
Victim Routine Influences the Number of DDoS Attacks: Evidence from Dutch Educational Network

We study the influence of daily routines of Dutch academic institutions on the number of DDoS attacks targeting their infrastructures. We hypothesise that the attacks are motivated and harness the postulates of Routine Activity Theory (RAT) from criminology to analyse the data. We define routine periods in order to group days with similar activities and use 2.5 years of NetFlow alerts data measured by SURFnet to compare the number of alerts generated during each of these periods. Our analysis shows clear correlation between academic schedules and attack patterns on academic institutions. This leads us to believe that most of these attacks are not random and are initiated by someone who might benefit by disrupting scheduled educational activities.
Visual Exploration of Topic Controversy in Online Conversations

Online conversations are often quite long with a lot of comments. Readers of these conversations may be interested in understanding how controversial the discussion is and how the differences of opinions arise for different topics. In this work, we are combining natural language processing with information visualization to support readers in exploring disagreements in online conversations. We present an initial prototype and discuss the possible directions for future work.
Deep Hashing and Its Application for Histopathology Image Analysis

Content-based image retrieval (CBIR) Content-Based Image Retrieval (CBIR) has attracted considerable attentionAttention for histopathology image analysisHistopathology image analysis because it can provide more clinical evidence to support the diagnosis. Hashing is an important tool in CBIR due to the significant gain in both computation and storage. Because of the tremendous success of deep learningDeep learning, deep hashingDeep hashing simultaneously learning powerful feature representations and binary codes has achieved promising performance on microscopic images. This chapter presents several popular deep hashingDeep hashing techniques and their applications on histopathology images. It starts introducing the automated histopathology image analysisHistopathology image analysis and explaining the reasons why deep hashingDeep hashing is a significant and urgent need for data analysis in histopathology images. Then, it specifically discusses three popular deep hashingDeep hashing techniques and mainly introduces pairwise-based deep hashingDeep hashing. Finally, it presents their applications on histopathology image analysisHistopathology image analysis.
The structure of a sliding sensory system for a self-adaptive prehensor

The quality of the prehension is determined by the mobility state of the prehension object.The emphasis on sliding within the self-adaptive prehensor, proposed in the paper, is achieved by correlating the parameters generated by the rotation sensor (encoder), the contact sensor and the angular position of the stepper motor.The rotation of the encoder is influenced by the active and passive mechanical components in the prehensor jaws.In this way, it is obtained a 2D sliding sensory system. The sensory system is a complex mechatronic system that can adapt to most industrial robots of different sizes and types, including the cartesian type. The prehension of the objects is accomplished through a sensory technique which integrates all software and hardware operations to achieve the image of the two directions grip. The sensory system has a resistive position sensor in its structure which determines the angular position of the axis of rotation of the execution element. The inductive position sensor is the second sensor of the system. It highlights the mechanical tensionless contact on the prehensile object. The sliding of the object is emphasized by a resistive position sensor. An important role in programming the prehensor is the correlation between the software information from the three sensing elements with the stepper motor that achieves the force required for the prehension.
Density of Top-Layer Codes in Deep Convolutional Neural Networks Trained for Face Identification

Deep convolutional neural networks (DCNNs) are the state-of-the-art in automatic face recognition and currently outperform humans on some tasks. These networks perform tens of millions of neuron-like computations to produce a “top-layer” face representation that is highly compact. This representation supports robust face identification, while retaining information about face gender and viewpoint. We tested the density of information encoded in the top-layer face representation by sub-sampling feature units and measuring the cost to identification accuracy as the number of units decreased. Using a 101-layer ResNet-based DCNN trained for face recognition (Ranjan et al., 2017), subsets of features (N = 512, 256, 128, 64, 32, 16, 8, 4, 2) were selected randomly without replacement from the full 512-dimensional top-layer feature set. Identification accuracy was tested using the IJB-C dataset (141,332 unconstrained images, 3,532 identities) and measured by the area under the ROC curve (AUC). Face identification performance declined at a remarkably slow rate as the subset size decreased (AUCs: 512, 256, 128, 64 features=0.98, 32 features=0.97, 16 features=0.95, 8 features=0.91, 4 features=0.85, 2 features=0.75). Next, we used the subsamples to predict the viewpoint and gender of the input faces. Viewpoint prediction error (in degrees) and gender prediction accuracy (percent correct) were measured as a function of decreasing subset size. Performance declined more rapidly for viewpoint and gender than for identity. Viewpoint prediction error increased from 7.87°, with the full feature set, to chance (~16°), with subsamples of 64 features or fewer. Gender prediction accuracy decreased from 95.0%, with the full feature set, to near chance (~70%) with subsamples of 32 features or fewer. This indicates that DCNN face representations require input from only a small number of features for successful identification, but require coordinated responses from the full complement of top-layer features to predict image information.
Model-Based Prediction of the Size, the Language and the Quality of the Web Domains

This article investigates possibilities for developing a model, which without completely downloading domains, is able to predict: (1) the size of domains, (2) the domains main language (as Albanian or not), (3) the multilingual domains, where at least one of the languages is Albanian, and (4) the quality of a domain. Proposed model excludes domains which are not written in Albanian from a given set of domains and ranks exclusively Albanian domains by their importance. Consequently, presented model offers higher flexibility and efficiency to a search engine which tends to index the Albanian web.
Investment dynamics in innovation and new technologies in higher education institutions

In modern conditions of business, innovations and new technologies have a great significance for the development of highly educated institutions. Major changes are under the influence of information-communication technologies, the Bologna process, the Internet, new tools for improving innovation in education and training. Investing in new technologies and innovation is necessary. In this paper, through the results of the research and the statistical processing, the dynamics of the introduction of innovations and new technologies are presented in private and state faculties in the Republic of Serbia. The period analyzed is between year 2010-2017. During the observed period, investment in innovation and new technologies is growing from year to year. During the same period, the changes caused by the demands of students and trends in foreign faculties were presented and analyzed. In addition, the impact of the competition and the dynamics of introducing innovations under their influence have been addressed. Statistical data processing has confirmed that all these changes affect the higher education institutions and that the value of the investment grows year after year.
Saliency Detection in Images with Complex Background by End-to-End Sparse Maxout CNN

This paper proposes a saliency detection model for images with complex background based on the end-to-end sparse maxout convolutional neural network. We introduce the saliency detection model based on two key ideas. The first one is considering sparsity of the convolutional neural network. The second one is implementing the end-to-end architecture for the saliency detection. Experiment results on CAT2000, SALICON, MIT300 and iSUN demonstrate that the proposed method achieves state-of-the-art results in saliency detection and prediction tasks. Furthermore, the analysis of different saliency evaluation metrics related to the results of experiment is provided.
Temporal Smoothing: Discriminatively Incorporating Various Temporal Profiles of Queries

Document smoothing has been shown to play a critical role to deal with the zero probability problems in the query likelihood retrieval model. Unlike traditional approaches using the same corpus language model for every document, the mainstream of the current methods introduce an additional smoothing item that can reflect the content of each document. However, these methods would either ignore the temporal characteristics of queries, or handle temporal factors in a multi-step process that cannot be explained in an unified solution, which rules out many potentially good alternatives. We instead propose a novel method, called temporal smoothing, which can alleviate the above problems. In particular, by using the overall temporal distribution of documents to smooth the temporal profile of a given query, the estimated temporal query model can be used in query likelihood retrieval model as an unified solution. Empirical evaluations based on a collection of Twitter documents and some standard benchmarks demonstrate the effectiveness of the proposed temporal smoothing mechanism in the retrieval task.
User Reputation Analysis for effective Trading on Bitcoin Network

Signed network is a refined form of social network where connections or relationships between people are defined with strength. An edge in this network can contain a positive, negative or neutral sign to define friendly, foe or neutral relationship respectively. This paper aims to utilize the properties of signed network and hence builds a Bitcoin alpha (whom-trust-whom) network. This trading network is further scrutinized to estimate user's reputation i.e. predicting ranks of participating actors. Thus, we are able to provide score to individual user to recommend whether he is safe to make transactions with. In addition, novel method for link prediction by calculating strength of path between two unconnected nodes is proposed. This strength value considers all the features of the nodes, edges and neighbors giving results that have higher accuracy over previously defined methods.
Risk Stratification and Prognosis Using Predictive Modelling and Big Data Approaches

Predictive modeling is the application of supervised machine learning methods to risk assessment and stratification, diagnosis, prognosis and therapeutics. With increasing availability of big biomedical data, predictive modeling is increasingly applied to leverage the data for clinical medicine, public health, and biomedical research. This chapter will describe key methods and application examples in the development, validation, dissemination and deployment of clinical predictive models.
Item Rating and Football Score Priori Prediction Algorithm

Attempts have been made to resolve various prediction issues relating to recommender system algorithms. In everyday life, there is a lot of need to predict something. For example, when you want to determine which items someone wants to buy or estimate the results of football matches or predict the rating of a movie. It's better if we can sell something that is in accordance with the wishes of the customer. Therefore, algorithm predictions are needed that can produce predictions accurately and precisely. In this paper, algorithm predictions are presented with prior data concepts, where data will be divided into 2 parts, training and testing dataset. There are 5 stages in the Priori Prediction (PP) algorithm starting from the data input process, checking the similarity of data, calculating using the difference function, calculating the predictive score, then evaluating using MAE.PP algorithm is also compared with other recommender system algorithms, which are collaborative filtering, cosine similarity, and content-based filtering in experiments and evaluations. The test results show that the proposed algorithm has satisfactory performance compared to other recommender system.
A Model for Extracting Most Desired Web Pages

Weblog analysis takes raw data from access logs and performs study on this data for extracting statistical information. This info incorporates a variety of data for the website activity such as average no. of hits, total no. of user visits, failed and successful cached hits, average time of view, average path length over a website; analytical information such as page was not found errors and server errors; server information, which includes exit and entry pages, single access pages, and top visited pages; requester information like which type of search engines is used, keywords and top referring sites, and so on. In general, the website administrator uses this kind of knowledge to make the system act better, helping in the manipulation process of site, then also forgiving marketing decisions support. Most of the advanced web mining systems practice this kind of information to take out more difficult or complex interpretations using data mining procedures like association rules, clustering, and classification.
If I understood you, would I have this look on my face?

Best known for being a prankster in the 1970s television series, Mash, Alan Alda has found a new way to make us laugh: at ourselves. Prompted by a personal and costly miscommunication, Alda sets ou...