Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Classification of Microtubule Ends: An Evaluation of Machine Learning Approaches

Aim of this thesis was to evaluate the performance of three popular machine learning methods – decision trees, support vector machines, and neural networks – on a supervised image classification task from the domain of cell biology. Specifically, the task was to classify microtubule ends in electron tomography images as open or closed. Microtubules are filamentous macromolecules of the cytoskeleton. Distribution of their end types is of interest to cell biologists as it allows to analyze microtubule nucleation sites. Currently classification is done manually by domain experts, which is a difficult task due to the low signal-to-noise ratio and the abundance of microtubules in a single cell. Automating this tedious and error prone task would be beneficial to both efficiency and consistency. Images of microtubule ends were obtained from electron tomography reconstructions of mitotic spindles. As ground truth data for training and testing four independent expert classifications for the same samples from different tomograms were used. Image information around microtubule ends was extracted in various formats for further processing. For all classifiers we considered how the performance varies when different preprocessing techniques (per-feature and per-image standardization) are applied. or decision trees and support vector machines we also evaluated the effect of training on a) imbalanced versus under- and over-sampled data and b) image-based vs feature-based input for specifically designed features. The results show that for decision trees and support vector machines classification on features outperforms classification on images. Both methods give most equalized per-class accuracies when the training data was undersampled and when preprocessed with per-image standardization prior to features extraction. Neural networks gave the best results when no preprocessing was applied. The final decision tree, support vector machine, and neural network obtained accuracies on the test set for (open,closed ) samples of (62%, 72%), (66%, 70%), and (61%, 78%) respectively, when considering all samples where at least one expert assigned a label. Restricting the test set to samples with at least three agreeing expert labels raised these to (78%, 84%), (74%, 92%), and (82%, 88%). It can be observed that many samples misclassified by the algorithms were also difficult to classify for the experts.
A Review and Survey of Various Attribute based Data Storage & Deduplication in Cloud

Data deduplication implies to the method that reduces the unnecessary data on the data centres and the send it on the network and it will be one of the most-enabling storage technologies that presents well-organized reserve development in the cloud computing. On the additional hand, be appropriating data deduplication acquires protection vulnerabilities in the cloud storage method so that unauthorized attributes including a cloud server or unknown users may break data confidentiality, privacy and integrity on the outsourced data. It is difficult to resolve the difficulty of data defense and confidentiality regarding data de-duplication, but positively essential for presenting amature and established cloud storage service. Here in this paper survey of all the existing techniques that are implemented based on Attribute based Encryption Data Storage and Deduplication over Cloud Computing, hence on the basis of various limitations a new and efficient technique is implemented in future.
Architecture Model of Integrated Web-based Etrading Business Process Management System

— This paper describes development of architecture for integrated informational Web-based e-trading business process management system. An approach is justified for formation of embedded informational system architecture based on typical IT solutions. A Service Oriented Architecture (SOA) using Enterprise Service Bus (ESB) concept is offered for architectural implementation of the Web-based system. Typical informational systems that belong to the Web-system are used as services. The web-services implement SOAP protocol based on WSDL (Web Services Description Language) and UDDI (Universal Description, Discovery and Integration) specifications. Application of ESB and SOA provides scalability of Web-based e-trading management system, possibility for their enhancement and upgrading. The principle of basic architecture of Internet has been implemented in the architecture of integrated trading informational system taking into account transition to informational level.
Application of Supervised Machine Learning to Predict the Mortality Risk in Elderly Using Biomarkers

The idea of long-term survival amongst older individuals has been a major medical and social concern. A wide range of biomarkers have been identified to prospectively predict disability, morbidity, and mortality outcomes in older adult populations. The machine learning techniques applied with clinically relevant biomarkers provide new ways of understanding diseases and solutions to tackle challenges to the health of the aging population. This paper describes two supervised machine learning techniques, Logistic Regression (LR) and Support Vector Machine (SVM) which are used in the prediction of the mortality in elderly people. LR is one of the traditionally used predictive modeling methods in clinical research where the probability of occurrence of two classes is a dichotomous criterion whereas, SVM is an emerging classification supervised learning technique based on building models using maximum-margin hyperplane. An attempt has been made to measure the classifier accuracy of each model and the performance of both the models is compared on a set of biomarker features of old patients. The experimental result shows that the SVM model outperformed the LR model in the prediction of survivorship among old individuals with statistically significant results (p<0.01).
Influence of Variable-Length Speech Units on Quality of Synthetic Speech Signal

This paper examines variable-length speech units in the context of a single unit instance text-to-speech system. Experiments described here were intended as a preparation for our future multiple unit instance system. Subphoneme, phoneme and word units were studied to find out the influence of variable-length units on the synthetic speech in a fully working TTS system. Statistical approach was applied to build variable-length speech unit databases automatically. Longer units were shown to be superior because the synthetic speech contained less spectral discontinuities in comparison with shorter units. Our attention was also paid to a level of clustering speech context in the process of automatic speech unit modeling. Model-level clustering was shown to outperform state-level clustering.
Performance Evaluation of DV-HOP and Amorphous Algorithms based on Localization Schemes in Wireless Sensor Networks

In the field of high-risk observation, the nodes in Wireless Sensor Network (WSN) are distributed randomly. The result from sensing becomes meaningless if it is not known from where the originating node is. Therefore, a sensor node positioning scheme, known as the localization scheme, is required. The localization scheme consists of distance estimation and position computing. Thus, this research used connectivity as distance estimation within range free algorithm DV-Hop and Amorphous, and then trilateral algorithm for computing the position. Besides that, distance estimation using the connectivity between nodes is not needed for the additional hardware ranging as required by a range-based localization scheme. In this research compared the localization algorithm based on range free localization, which are DV-Hop algorithm and Amorphous algorithm. The simulation result shows that the amorphous algorithm have achieved 13.60% and 24.538% lower than dv-hop algorithm for each parameter error localization and energy consumption. On node density variations, dv-hop algorithm gained a localization error that is 26.95% lower than amorphous algorithm, but for energy consumption parameter, amorphous gained 14.227% lower than dv-hop algorithm. In the communication range variation scenario, dv-hop algorithm gained a localization error that is50.282% lower than amorphous. However, for energy consumption parameter, amorphous algorithm gained 12.35%. lower than dv-hop algorithm.
Adaptive Wireless Video Streaming Based on Edge Computing: Opportunities and Approaches

Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users’ channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.
Artificial Dasein: Solving the Frame Problem with Incremental Contextualization

In this paper, we give an account of a rationally behaving agent—an artificial Dasein—which can perceive the world in terms of relevance to its own goals. The way we achieve this is through a process of incremental contextualization of goals and constraints that range from the purely conceptual and abstract to the well-defined and physical. The model described in this paper combines a conceptual hierarchy with a schema structure, and leads to an account of practical reasoning which relies on two novel ideas: the recursive selection of increasingly contextualized subgoals, and the tractable determination of behavioral consequences through simulation. The present account seeks to provide an outline for developing an agent which does not suffer from the frame problem due to the way in which it incrementally contextualizes its goals until they can be achieved unreflectively by matching them to pre-learned schemas. We believe that this account can lead to a form of artificial intelligence more powerful than traditional attempts based on formal logic.
Degrees of Agency in Owners and Users of Home IoT Devices

Internet of Things (IoT) devices are slowly populating our homes. In th is age of shar ing econom y and increased mobility, however, the home environment is no longer a fixed location always shared by the same people . To better understand the issues and challenges around agency and IoT use in the home , we take a p ragmatic and situated approach . In this paper, we draw on our own experience s as users and identify the tensions between ownership and usage , and the economic implications there might be when sharing IoT system s with trusted people vs. strangers . We suggest the distinction between owners and users should be more carefully considered in the design and research of future device.
Initial thoughts on comparing computational design creativity systems

Abstract This paper describes an initial investigation into models that might be useful to describe and compare computational design creativity (CDC) systems. An existing approach to creative system comparison, the FACE model, is critiqued, and discovered to be a weak match with design systems. Informed by this discovery, an alternative more design-oriented, ontology-based approach is proposed that describes systems in terms of design-oriented processes. Various proposals in the literature about design reasoning processes are evaluated for inclusion in an ontology of processes that could be used for CDC comparison, using criteria that are developed and presented in this paper.
Malware Family Detection Approach using Image Processing Techniques: Visualization Technique

: - The risk of malicious software has increased a lot since last decade as the use of internet has increased drastically. According to Avast Test report, 22,000 to 25,000 new malware have been reported every day. Even though huge malwares having different structures are introduced every day, their nature of working is almost similar to old malwares. The malwares with the similar functionalities are considered to be the member of the same family. Classification and detection of malware family are important to design its signature of anti-malware software. In this article, we represent the concise study carried out on detecting various malware family. This paper mainly focus on visualization technique for classifying malware family. Visualization technique uses image processing approaches to classify the malwares. The malware executable binary files are transformed into image and this images are used to detect the family of malware.
"Lo-Fi to Hi-Fi" Crowd Cataloguing: Increasing E-Resource Records and Promoting Metadata Literacy within WiderNet

Five billion people, as well as rural libraries, schools, and prisons across the globe, are not connected to the Internet. The WiderNet Project attempts to assist these underserved populations with its eGranary Digital Libraries, which make a wide variety of Web resources available offline. Access alone is not enough, however, as an individual’s first exposure to Web resources is much like ‘drinking from a firehose’. Additionally, most of the eGranary’s 32 million resources remain uncatalogued and hence difficult to find. To address this issue, we are experimenting with ‘crowd cataloguing’ to create and cull cataloguing information for eGranary resources. An exploratory study is introduced in which we will draw on lessons learned from folksonomy as well as previous eGranary cataloguing projects. The purpose is to further develop a “Lo-Fi to Hi-Fi” model of eGranary metadata generation that emphasizes improving the metadata literacy of general users and library professionals.
Cultivating Political Incivility

Various scholars, pundits, and journalists, along with a majority of the American public believe incivility in politics is increasing. Nevertheless, the Annenberg Public Policy Center’s (APPC) baseline measure of political incivility remains relatively flat. How can these incongruous conclusions be reconciled? This article suggests that perceptions of political incivility are a function of viewing distinct genres of television news media: “hostile” cable news versus network news. Using a content and secondary data analysis, this article demonstrates that (1) cable news outlets provide more coverage of elite political incivility than do network news outlets and that (2) cable news viewers perceive politics to be more uncivil than do network news viewers. Combined with previous research, the results suggest cable news viewers may perceive more incivility in national politics than actually exists. Theoretically, this article provides support for genre-specific media cultivation effects. Implications for cultivation theory and incivility are discussed.
The spread of misinformation by social bots

The massive spread of digital misinformation has been identiﬁed as a major global risk and has been alleged to inﬂuence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in eﬀorts to study the complex causes for the viral diﬀusion of misinformation online and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these eﬀorts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We ﬁnd evidence that social bots play a disproportionate role in spreading and repeating misinformation. Automated accounts are particularly active in amplifying misinformation in the very early spreading moments, before a claim goes viral. Bots target users with many followers through replies and mentions, and may disguise their geographic locations. Humans are vulnerable to this manipulation, retweeting bots who post misinformation. Successful sources of false and misleading claims are heavily supported by social bots. These results suggest that curbing social bots may be an eﬀective strategy for mitigating the spread of online misinformation.
Research Survey on Support Vector Machine

Support vector machine (SVM) as a learning machine has shown a good learning ability and generalization ability in classification, regression and forecasting. Because of its excellent learning performance, SVM has always been a hotspot in machine learning. In hence, this paper will make a more systematic introduction of SVM, including the theory of SVM, the summarization and comparison of the quadratic programming optimizations and parameter optimizations for SVM and the introduction of some new SVMs, like FSVM, TSVM, MSVM, etc. Then, the applications of SVM in real life will be presented, especially, in the area of mobile multimedia. Finally, we conclude with a discussion of the direction of further SVM improvement.
Computer assisted language learning and the internationalisation of the Portuguese language in higher education contexts

The internationalisation of the Portuguese language has become a priority for academic institutions of different Portuguese-speaking countries which are trying to adapt to the current context of globalisation and ubiquitous communications through digital media. In order to achieve it, several challenges should be faced, namely providing international students with resources aimed at helping them improve their Portuguese while fostering its use in academic and scientific contexts. This can be done by identifying, creating and editing high-quality teaching materials which bear in mind the sociocultural, political, economic and linguistic contexts; using the Information and Communication Technologies effectively in both face-to-face and online learning environments (International Institute of the Portuguese Language, 2010); and promoting the online learning of Portuguese as a Foreign Language in higher education settings through virtual learning environments. This paper explores the challenges brought about by the internationalisation of Portuguese and how computer assisted language learning - and, more specifically, the development of a virtual learning environment aiming at catering for the linguistic needs of international students in different institutions in Brazil, Portugal and elsewhere - could help face these challenges.
Online Generation and Use of Macro-Actions in Forward-Chaining Planning

This thesis presents a technique for online learning and management of macroactions in forward chaining planning. Macro-actions are learnt on plateaux, areas of the search landscape where the heuristic cannot offer good search guidance, and are reused when future plateaux are encountered. Libraries of macro-actions are generated, storing macro-actions for use on future problems. Several strategies for the management and pruning of such libraries are considered allowing the planner to maintain a smaller collection of macro-actions to minimise potential negative effects on performance. The work is extended to investigate the potential for the simulation of macroactions without increasing the branching factor during search. Actions are reordered during enforced hill-climbing (EHC) search, and best-first search, based on the number of times they have followed the action currently at the tail of the plan in past solutions. In EHC this is equivalent to suggesting two-step macroactions without increasing the branching factor. The techniques are evaluated across a wide range of domains with differing properties under the relaxed planning graph heuristic. The results show that a library of plateau-escaping macro-actions can improve planner performance using only online learning techniques to select the best macro-actions to keep. Search time pruning is shown to be very effective; whilst pruning the library of macro-actions based on the number of times each action is used can offer further improvements. Action reordering has been shown to offer performance improvements, albeit not as great as those produced by using macro-actions, but without the occasional degradation in performance on some problems that is often associated with macro-actions.
Smart Bin for Incompatible Waste Items

In this paper, we present a generalized ontology based system to identify the necessary properties of products and objects and then make inferences of possible incompatibil- ities between them. This is designed in the context of waste management, where considerable volume of waste is present collectively and there may be chances of hazard or reduces the value of the recyclable waste. These inferences for the group of objects are performed based on the knowledge available locally without looking up from any external sources. Since, the global identification of objects is avoided, preservation of privacy is ensured, which is a concern in the field of pervasive computing. Our model can have applications in various domains. We have demonstrated it's application in the domain of waste management and discussed about other possible domains.
Sound processing for a bilateral cochlear implant system

The invention discloses sound processing for a bilateral cochlear implant system, and a method for producing stimulation pulses in a bilateral cochlear implant (CI) includes receiving a sound at a first microphone or a first microphone array and receiving the sound at a second microphone or a second microphone array; generating a first microphone signal in response to the sound received at the first microphone or first microphone array and generating a second microphone signal in response to the sound received at the second microphone or second microphone array; filtering the first microphone signal into a plurality of band limited first microphone signals and filtering the second microphone signals into a plurality of band limited second microphone signals; determining a major sound and extracting the direction of arrival of the major sound; based on the determined major source, generating a primary pulse pattern; and generating a copy including the generated primary pulse pattern and a secondary pulse pattern with positioning information contained therein, the positioning information being based on the extracted direction of arrival.