Reviewerly/exHarmony huggingface dataset:
Designing for conflict

Public display networks are increasingly seeking to provide personalised content to viewers. A key challenge in this area is to how to support conflicting personalisation requirements from multiple concurrent viewers. In this paper we present a four dimensional design space that describes a wide range of approaches to addressing this issue. This design space can be used by developers and researchers to understand how systems can be engineered to deliver an increase in the effective communications bandwidth to viewers while meeting their conflicting content requirements. This is the first attempt at systematically examining the challenges and design choices in this important emerging area.
Automatic Summarization Method of Technical Literature Based On Domain Ontonogy

In the field of automatic summarization technology, it is hot but difficult to make full use of the semantic information of text. Owing to the semantic reasoning of ontology, we proposed an automatic summarization method based on domain ontology. The domain ontology is constructed by using knowledge graph as the initial template and the FP-Growth association rule to mine the relations between conceptions. After mapping sentences to RDF triples, the final triples will be given by semantic reasoning based on domain ontology. The summary of technical literature based on domain ontology express the themes more accurately. Taking ROUGE as the evaluation, the method we give is better than the traditional auto-summarization method in average, the ROUGE value of multiple documents summarization is 15% higher than single document summarization.
Weakly Supervised Fine-grained Recognition in a Segmentation-attention Network

Fine-Grained recognition plays a significant role in many fields such as cancer cell detection, identification of crop diseases and insect pests. It is a challenging mission due to the difficulty of the lights, position, viewing angle of object and complex foreground. Only the object itself that can provide useful and significant information to handle this issue, however existing methods mainly process this question by acquiring discriminative regions to extract the feature for training while ignoring other vital features of the object. In this paper, we propose a novel weakly supervised method that can segment the mainly object from complex background and extract object features with bilinear attention network for prediction. Compared to the previous weakly supervised methods, our approach can avoid the effect of noisy background and fully extract the object feature. Furthermore, we design a Bilateral filtering module for removing the rest of noisy background and get the uniform and robust features. Additionally, attention mechanism is also utilized in feature extraction. A series of experiments on publicly available dataset CUB-200-2011 and Oxford flowers 17 show that our method is effective for finegrained recognition, with relative accuracy gains of 0.98% and 2.23%, respectively..
An efficient Algorithm for medical image classification using Deep Convolutional Network

Automatic classification of medical images especially of tissue images is an important task in computer aided diagnosis (CAD) systems. Deep learning methods such as convolutional networks (ConvNets) outperform other state of-the-art methods in images classification tasks.
Privacy, Cybersecurity & ISO 27001: How Are They Related | I.S. Partners

Even the most seasoned IT leaders have to occasionally stop and remind themselves of all the aspects of computing they need to keep straight. Over the ...
Apache Spark Implementations for String Patterns in DNA Sequences

The availability of numerical data grows from 1 day to another in a remarkable way. New technologies of high-throughput Next-Generation Sequencing (NGS) are producing DNA sequences. Next-Generation Sequencing describes a DNA sequencing technology which has revolutionized genomic research. In this paper, we perform some experiments using a cloud infrastructure framework, namely, Apache Spark, in some sequences derived from the National Center for Biotechnology Information (NCBI). The problems we examine are some of the most popular ones, namely, Longest Common Prefix, Longest Common Substring, and Longest Common Subsequence.
Disseminating Scientific Development Through Artistic Practice: The HCI History Poster

This paper presents two contributions: (i) an algorithm for generating visualizations of the historical interfield relations of a topic within a scientific corpus by parsing scientific literature and linking it using citation metrics, and (ii) a poster generated using aforementioned algorithm, that depicts the historical development of 'interaction' within the field of Human-Computer Interaction, based on all CHI papers and their citation data from Google Scholar. Furthermore, we discuss possibilities and limitations of disseminating scientific developments through artistic practice.
A Novel Training Mechanism for Extending Convolutional Neural Network

Convolutional Neural Network (CNN) has obtained great success in the computer vision domain in the recent years. These CNN models adopt deeper neural network architecture to achieve high recognition accuracy, the training costs of time and dataset are dramatically increased. While the recognizing categories are expanded, the CNN architecture needs to be modified, the whole CNN model requires to be retrained. Transfer learning method is adopted to save the training cost by migrating part of learned weights, from the existed CNN model to the target CNN model with expanded recognizing categories. However, the requirement of modifying neural network architecture still consumes huge amount of the training cost. This paper presents a new training mechanism, called Extended Learning, to solve the above problems. By using the proposed Partially Back-Propagation Operation, the CNN model can expand new classification categories without modifying the architecture of the CNN model, the learning weights from previously training results can be retained, the training cost of time and dataset can be reduced accordingly. The experimental result shows that the proposed extended learning method can save 16.7% training image count compared to the transfer learning method, with the target accuracy of 0.75.
Person Re-identification Based on Spatially Constraints and Kernel Consensus PCA

Person re-identification (Re-ID) aims at pedestrian matching and ranking across non-overlapping field-of-view. However, Re-ID is a challenge work due to the variations in resolution, pose, illumination and camera viewpoint. We learn a measurement score, which consists of multiple sub-similarity measurements with each taking in charge of a sub-region. In particular, the measurement function combines the similarity function and the distance function, so it exploits the complementary strength of the two functions. Dense color and texture features are extracted to describe the person image, and consensus PCA, which captures a consensus projection between multi-block data, is employed to deal with the features in a linear space. Furthermore, kernel consensus PCA is proposed to settle camera transition and dimensionality reduction in the non-linear space. The experimental results on four benchmark show significant and consistent improvements over the state-of-the-art methods.
Classification of Virtual Machine Consolidation Techniques: A Survey

Cloud computing is an on-demand technology for several IT infrastructures due to many of its aspects. One such aspect is virtualization, which is used for providing platform to deal with resource utilization, workloads, etc. Large data centers emit enormous amount of energy, and virtual machine consolidation is an effective technique to reduce the carbon footprints of data centers. VM consolidation accommodates virtual machines into a less number of physical machines and puts an underutilized server to hibernation mode. This paper contributes novel taxonomy of virtual machine consolidation techniques. We have derived the comparison matrices which represents the comparative analysis of performance matrix, issues resolved and mathematical models used by different VM consolidation techniques for making efficient consolidation decisions. This survey will also be helpful to the researchers intending to work for the development of decision support system for energy consumption minimization and to achieve good quality of service.
Using guessability framework

Mid-air gestures have been heavily studied in HCI but with mostly younger adults (YAs). Older adults (OAs) can equally benefit from such a modality, but given their heterogeneous motor abilities, designing suitable gestures is challenging [2]. Our research specifically looks at age-related differences in hand gesture preferences between older and younger adults. This subject is important since it relates to the idea of a proper age-inclusive technological design and the means towards the successful adoption of technologies by all the layers of the population, including older adults [1].
A Kernel Correlation Filter Tracker with Fast Scale Prediction

Most existing scale solutions of Correlation Filter-based trackers fail to consider the priority of target scale calculation. Their high complexity destroys the high speed performance of the tracking method. To tackle this problem, an optimization strategy of combining scale prediction and scale pool is proposed. This method predicts the direction of future scale change by collecting the information of target historical scale variations, further divides the scale calculation priority, and uses the response characteristics of the correlation filter tracker to determine optimal scale. Both quantitative and qualitative experiments show that the proposed approach can achieve fast estimation of the target scale. The high real-time performance provides a guarantee for the migration of correlation filter target tracking algorithm to the development board with low computing capacity of embedded system.
Fact-checking algorithms for the Internet

Abstract Verification of facts refers to the task of verifying textual content. Taking into account the growth of false information on the Internet, fact-checking becomes not only the activity of only journalists. The problem in computer-based fact-checking on the Internet is the lack of universal mathematical models for extracting useful textual information. Proposed the algorithms and mathematical models for extracting useful textual information in order to further verify facts. Presented results of experiments with the practical application of the developed mathematical model for extracting useful text from Internet resources.
BIOSTRATIGRAPHIC CORRELATION OF PALEOZOIC ROCKS IN NORTHERN AND WESTREN IRAQ

The Paleozoic rocks outcropped in northern Iraq (Ora, Chia Zairi section) are biostratigraphically investigated for their microfossils content. Benthonic foraminifera and algal genera characterize the upper part of the section while palynomorphs (Miospores &amp; Acritarchs) dominated the lower part the section which is consist of clastic sediments. The study also include the stratigraphic succession of the section and its correlation to the subsurface sections penetrated in oil and water wells drilled in the western desert of Iraq in order to determine the regional distribution of the economically important formations, either as reservoir or as source rocks for hydrocarbons. Index palynomorphs including many types of Acritarch genera are identified in the Khabour Formation indicate lower Ordovician time (Tremadocian? Arenigian –Llanvirnian: age) The Perispik Formation has been found barren of any type of microfossils and is composed of red clastic rocks.&#x0D; Pollen and Spores are recorded from the "Ore Group" (Kaista, Ora and Harur Formations ) indicate that these rocks are of Upper Devonian – Lower Carboniferous (Famennian – Tournaisian age ) .A Large number of foraminifera and Algal genera are identified in the Chiazairi rocks,these genera and species are indicated the Upper Permian rocks of Thuringian age.&#x0D; The stratigraphic succession of the Paleozoic section studied in northern Iraq indicate that there is a stratigraphic break represents by missing of the Ga'ara Formation (late Carboniferous –early Permian) and Akkas Formation (Silurian) and the upper part of the Khabour Formation (Upper Ordovician).
Shared Word Embedding Space Modeling Method Based on Orthogonal Projection

With the continuous development of computer technology, machine learning has been applied in more and more fields. However, the application of word embedding technology in bilingual Chinese and English still needs to be developed. In this paper, we propose a model construction process based on orthogonal projection, and analyze the validity of the model from multiple perspectives. We carry out word sense similarity experiments and word analogy experiments for the quality of single language in the model, and cross-language text similarity experiments for different linguistic quality in the model. Through the analysis of the experimental results, it can be proved that the proposed shared word embedding space model achieves good results compared with the traditional word embedding model, and the effect of the model achieves the desired purpose.
Prediction-Based Joint Energy Optimization for Virtualized Data Centers

Today's data centers tend to have tens to hundreds of thousands of servers to provide massive and sophisticated services. Statistically, data center and data center networks (DCNs) remain highly underutilized which can be exploited for energy-saving. In this paper, we have studied energy-saving problem for network and server sides of the data center. The problem was formulated as a Mixed Integer Linear Program (MILP) that is solvable by an optimization software to jointly minimize the energy consumed by the servers and DCN. To overcome the optimization software high computational time, a heuristic algorithm to provide practical and efficient solution is introduced. The heuristic algorithm has two stages: first, it uses the virtual machines (VM) and the predicted servers resource utilization to provide VM consolidation algorithm and turn-off unused servers. The second stage uses an abstract performance-aware network flow consolidation that focused the traffic on subset of the network and turn-off the unused network devices. Simulation experiments using CloudsimSDN were conducted to validate the heuristic using real traces from Wikipedia in terms of energy consumption and average response time. The results show that the heuristic can save servers and network energy while maintaining performance.
Tool to Extract and Summarize Methodologies of Research Articles for Visually Impaired Researchers

With the development of technology, not only the university students but also visually impaired students have also started engaging in higher studies and research. They have started using electronic documents comprehensively for their studies. However, some components of electronic documents are also in a difficult state to be referred as per their complexity. This paper has introduced an application to identify such components and to summarize the research methodology section of Information Technology related research papers including the flow charts and graphs. The requirements of visually impaired students in research were identified and the paper has introduced a system to help these students by summarizing methodologies of a given paper within a very short period of time accurately in the document form and in voice. The methods such as machine learning, Information extraction and natural language processing were mainly applied to bring the solution out. Currently available applications were thoroughly observed and this concept was introduced with several features that are lacking in the existing solutions. The results were observed via visually impaired researches with their consent on the support provided by the output.
Automation Process to Support an Information System on Extreme Weather Warning

Abstract Hydrometeorological hazard early warning system has been developed in order to support public society in anticipating the extreme weather in Indonesia. Meanwhile, the early warning system in Indonesia still produced locally and need arduous human resources to process the enormous size data to have a good quality early warning. Therefore, in order to increase the effectivity, efficiency, and efficacy of the early warning system to produce the desired result, the automatic process was needed. This study will explain further the mechanism of process automation from the weather forecast to the output of warning displayed in an information system. The automation process combines several scripting in Python and batch script for GIS processing, as well as process scheduling using task scheduler. The output of extreme weather warning is in form of JSON files stored in organized folder directory. The automated process proven to be more useful in increasing the effectivity, efficiency, and efficacy of the early warning system to produce the desired result and later the automated process could be enhanced further to boost the lead time and reducing the arduous task in producing the early warning system.
Harnessing Gestures to Externalize Discourse Ideas for Common Ground: Design Implications from a Frame of Reference Analysis

When we share ideas through conversation, we convey a specific mental image to another person. However, mental images don't always match, and the effort it takes to stop and achieve common ground would interrupt conversational flow. We are currently investigating the design of a system that translates a speaker's gestures and speech into a visualization of their idea. The challenge of designing such a system is complex, as conversation is composed of many intricate factors. We focus the results in this paper on one factor in particular: speaker of reference. We ran a study with 26 participants, with a prototype meant to handle gestured descriptions of object size and noted the effects of speaker frame of reference. From our analysis of reference frames used during size descriptions, we draw implications for how our proposed system may detect and translate frame of reference to produce visualizations of the user's mental image.
Hybrid Encoding-based Model for Chinese Reading Comprehension

Machine reading comprehension has been an elusive challenge in the study of machine intelligence in recent years. The cloze-style reading comprehension is a query containing a placeholder that should be replaced with an appropriate word based on reading and understanding of a related document. This paper presents a novel hybrid encoding-based model for the cloze-style Chinese machine reading comprehension task, which resembles the human brain reading and reasoning process. The model is evaluated with the Children's Fairy Tale corpus used in The First Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC2017). With an accuracy of 78.7% on the test set, this model substantially outperforms the comparison models published in CMRC2017. Also, we conduct extensive experiments on People's Daily Online, People's Daily, and Reference News datasets, and the experimental results dramatically improve a stable baseline for the Chinese machine reading comprehension task.
Fusion of Skin Color and Facial Movement for Facial Expression Recognition

In this paper, based on the combination of skin color and facial movement, the automatic expression recognition algorithm is studied. The image is transformed into YIQ color space by RGB, and image data is extracted in the first dimension of YIQ. Background and skin color are segmented from binary image. Pareto optimization algorithm is used to select facial expression features. This algorithm has less computation, simple structure and fast running speed. It can accurately detect and track the small-angle facial skin color, facial expression changes, facial rotation and occlusion. Experiments show that the proposed algorithm can be well adapted to face rotation at small angles. For the state of human eyes, the algorithm is not affected, and can adapt to the rich facial expression changes and different skin color better, and has a certain stability.
Illuminance Compensation and Texture Enhancement via the Hodge Decomposition

Image brightness in color representation has caught active research interests, while its influence to texture features is relatively rarely studied. In this paper, we address the issue of illuminance, or brightness, interference to texture descriptors, especially to the Gabor filter based approaches. Firstly, we reveal the fact that the Gabor filter response linearly drifts with the illuminance. Secondly, the Hodge decomposition is introduced, and a linear regression model is presented to interpret the linear drift phenomenon. An interesting connection with the intrinsic image decomposition is established. Finally, a partial differential equation based method is proposed to compensate the drift issue. Consequently, the texture of the compensated image is enhanced. Extensive experiments are conducted to verify the proposed statements. The proposed linear regression model is verified empirically, and the compensation performances are demonstrated. Classification results on various databases indicate that the compensation can effectively improve the capacity of many existing texture description methods. Especially, the results show that the boosting effect on some deep learning based methods is also valid.
Live Video Streaming Optimization Based on Deep Reinforcement Learning

Video players employ adaptive bitrate algorithms in video-on-demand (VoD) scenarios to improve user-perceived quality of experience (QoE), whereas performance will obviously decline in live video streaming scenarios. To this end, we propose a novel deep reinforcement learning (DRL) based live video streaming optimization approach. Firstly, we point out the optimization objectives by comparing the difference between the VoD scenario and the live video streaming scenario. Then, according to the optimization conditions, we establish QoE optimization model in combination with a state-of-the-art DRL algorithm. We compare our algorithm with state-of-the-art ABR algorithms in a simulator with real-world video and network trace. Simulation results show that the proposed algorithm improves user experience quality by 5.6% on average, compared with existing algorithms.
Algorithms and methods of image classification for automated medical systems

Abstract Modern medicine in the diagnosis of diseases increasingly relies on the use of automated image recognition systems. Automation is of particular importance in the diagnosis of socially significant diseases, when the quality and speed of diagnosis is important. The pathology processes imposes certain restrictions on the methods used, which are associated both with image quality and with the speed of analysis. The article discusses the algorithms and methods for classifying medical images as the basis for automated diagnostic systems; Criteria for choosing among them those methods that satisfy the requirements of the task are determined. Based on a comparison of compliance with the criteria of the methods, neural network methods were selected: a neuro-fuzzy network, a convolutional neural network, and self-organizing maps.