Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of "AUTOSTRADA.INFO/RU" Users’ Comments

As a result of the analysis, it was revealed that social networks (Vkontakte, Facebook), thematic communities in microblogging networks (Twitter), resources for travelers (TripAdvisor), transport portals (Autostrada) are a source of up-to-date and operational information about the traffic situation, the quality of transport services and passenger satisfaction with the quality of levels of transport services. However, the existing transport monitoring systems do not contain software tools capable of collecting and analyzing traffic information located in the Internet environment. This paper discusses the task of building a system for automatically retrieving and classifying road traffic information from transport Internet portals and testing the developed system for analyzing the transport networks of Crimea and the city of Sevastopol. To solve this problem, an analysis of open source libraries for thematic data collection and analysis was carried out. An algorithm for extracting and analyzing texts has been developed. A crawler was developed using the Scrapy package in Python3, and user feedback from the portal http://autostrada.info/ru was collected on the state of the transport system of Crimea and the city of Sevastopol. For texts lemmatization and vector text transformation, the tf, idf, tf-idf methods and their implementation in the Scikit-Learn library were considered: CountVectorizer and TF-IDF Vectorizer. For word processing, Bag-of-Words and n-gram methods were considered. During the development of the classifier model, the naive Bayes algorithm (MultinomialNB) and the linear classifier model with optimization of the stochastic gradient descent (SGDClassifier) were used. As a training sample, a corpus of 225,000 labeled texts from the Twitter resource was used. The classifier was trained, during which the cross-validation strategy and the ShuffleSplit method were used. Testing and comparison of the results of the pitch classification were carried out. According to the results of validation, the linear model with the n-gram scheme [1, 3] and the vectorizer TF-IDF turned out to be the best. During the approbation of the developed system, the collection and analysis of reviews related to the quality of transport networks of the Republic of Crimea and the city of Sevastopol were conducted. Conclusions are drawn and prospects for further functional development of the developed tools are defined.
Ubicom applications and technologies

The motivation for working up this report was to provide the parties involved or interested in Ubicom activities with information about opportunities and enablers in the area, and to contribute Tekes in defining research topics for the current and future research programs dealing with Ubicom. The main approach in the report is from the application system viewpoint. The main emphasis of the technical enablers is in shortrange wireless communication technologies. The report deals with the evolution and different approaches to the Ubicom concept around the world, possible application areas and scenarios with general requirements concerning them, key technical enablers with an estimation of their applicability and development during the next few years, aspects for the planning and evaluation of the business models of application systems, and recommendations for the future research activities and leading commercial applications. The main suggestions of this report for the Ubicom research are: The research should be interdisciplanary, that is, it should combine human sciences, usability studies, business and economic research as well as legislation and ethical issues with the technology research, which is the basic enabler of the advancements in this area. Furthermore, both the application and the technology viewpoints must be combined. The core technology research should address short-range wireless communication, middleware, reasoning and smart algorithms, usability issues, as well as sensors and actuator technologies.
Automated two-phase business model-driven synthesis of conceptual database models

Existing approaches to business process model-driven synthesis of data models are characterized by a direct synthesis of a target model based on source models represented by concrete notations, where the synthesis is supported by monolithic (semi)automatic transformation programs. This article presents an approach to automated two-phase business process model-driven synthesis of conceptual database models. It is based on the introduction of a domain specific language (DSL) as an intermediate layer between different source notations and the target notation, which splits the synthesis into two phases: (i) automatic extraction of specific concepts from the source model and their DSL-based representation, and (ii) automated generation of the target model based on the DSL-based representation of the extracted concepts. The proposed approach enables development of modular transformation tools for automatic synthesis of the target model based on business process models represented by different concrete notations. In this article we present an online generator, which implements the proposed approach. The generator is implemented as a web-based, service-oriented tool, which enables automatic generation of the initial conceptual database model represented by the UML class diagram, based on business models represented by two concrete notations.
A Simple Framework for the Annotation of Small Corpora

This paper describes a framework for the annotation of discourse which consists of the combination of software tools and a tagset. Its main purpose is to create small corpora for action-research projects conducted by Second of Foreign Language teachers, including Content and Language Integrated Learning, in their classrooms. The framework intends to be a solution for this particular situation, which requires a high level of simplicity. Although in the literature of Corpus Linguistics there are some good frameworks and tagsets for the annotation of corpora, they are usually too complex for teachers who are not experts either in Corpus Linguistics or Discourse Analysis.
Discourse Representation Parsing for Sentences and Documents

We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993). Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents. We present a general framework for parsing discourse structures of arbitrary length and granularity. We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy. Experimental results on sentence- and document-level benchmarks show that our model outperforms competitive baselines by a wide margin.
Temporal Neuro-Fuzzy Systems in Fault Diagnosis and Prognosis

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of an manufacturing systems. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker , cement factory of Ain Touta " Batna, Algeria ".
Design of Optimized Reversible BCD Adder/Subtractor

—Reversible logic has emerged as one of the most important research area in the past few decades. Reversible or information lossless circuits have applications in nanotechnology, digital signal processing, communication, computer graphics and cryptography. It is also a fundamental requirement in the emerging field of quantum computing. In this paper an optimized method is proposed to realize a reversible Binary Coded Decimal (BCD) adder/subtractor circuit. In order to optimize the design, nines compliment gate (NCG) and BSCL gates are proposed. This proposed reversible BCD adder/subtractor is evaluated and optimized in terms of gate count, constant inputs and garbage outputs.
Enterprise modelling with CIMOSA: Functional and organizational aspects

Enterprise modelling is a central activity in enterprise engineering (EE) and enterprise integration (EI). In this paper, we first present our definition and recall fundamental principles of enterprise modelling. Then we focus on new developments of the CIMOSA modelling language, which is an event-driven process-based modelling language suitable for enterprise system analysis and model enactment. New features of the language concern a clear separation between the concept of processes (modelled as workflows) and the concept of agents or resources (modelled by state-machines). The link between the two concepts is materialized by functional operations (elementary actions) as well as resource capabilities and competencies. Another extension of the language concerns modelling of organization aspects. An example illustrates concepts and methods used in the modelling approach.
Symbolic z-transforms using DSP knowledge bases

The implementation of basic signal analysis and transforms for DSP (digital signal processing) with a symbolic mathematical program called Mathematica is described. By extending its capabilities through a proper representation of knowledge of discrete signals, operators, and transforms, Mathematica can take the forward and inverse z-transforms symbolically and apply the discrete Fourier transform (DFT) as a symbolic summation. In addition to the 150+ common mathematical operators and functions built into Mathematica, the rule-based package defines 23 systems and 8 basic signals. Six of these systems provide the ability to take forward and inverse z-transforms, DFTs, and DTFTs of multidimensional signal processing expressions. More importantly, this package allows the user to manipulate signals (functions or data) and systems (operators) as algebraic expressions. The user can convert symbolic signal processing expressions to graphical, numeric, and textual representations. <<ETX>>
Optimal Control and Coordination of Small UAVs for Vision-based Target Tracking

Author(s): Quintero, Steven Andrew Provencio | Advisor(s): Hespanha, Joao P | Abstract: Small unmanned aerial vehicles (UAVs) are relatively inexpensive mobile sensing platforms capable of reliably and autonomously performing numerous tasks, including mapping, search and rescue, surveillance and tracking, and real-time monitoring. The general problem of interest that we address is that of using small, fixed-wing UAVs to perform vision-based target tracking, which entails that one or more camera-equipped UAVs is responsible for autonomously tracking a moving ground target. In the single-UAV setting, the underactuated UAV must maintain proximity and visibility of an unpredictable ground target while having a limited sensing region. We provide solutions from two different vantage points. The first regards the problem as a two-player zero-sum game and the second as a stochastic optimal control problem. The resulting control policies have been successfully field-tested, thereby verifying the efficacy of both approaches while highlighting the advantages of one approach over the other. When employing two UAVs, one can fuse vision-based measurements to improve the estimate of the target's position. Accordingly, the second part of this dissertation involves determining the optimal control policy for two UAVs to gather the best joint vision-based measurements of a moving ground target, which is first done in a simplified deterministic setting. The results in this setting show that the key optimal control strategy is the coordination of the UAVs' distances to the target and not of the viewing angles as is traditionally assumed, thereby showing the advantage of solving the optimal control problem over using heuristics. To generate a control policy robust to real-world conditions, we formulate the same control objective using higher order stochastic kinematic models. Since grid-based solutions are infeasible for a stochastic optimal control problem of this dimension, we employ a simulation-based dynamic programming technique that relies on regression to form the optimal policy maps, thereby demonstrating an effective solution to a multi-vehicle coordination problem that until recently seemed intractable on account of its dimension. The results show that distance coordination is again the key optimal control strategy and that the policy offers considerable advantages over uncoordinated optimal policies, namely reduced variability in the cost and a reduction in the severity and frequency of high-cost events.
Optimization of word-line booster circuits for low-voltage flash memories

Two word-line booster circuits, which output a word-line voltage for reading dash memory data, are analyzed and optimized. A capacitor-switched booster circuit outputs a voltage higher than the supply voltage by switching the connection state of one of more boosting capacitors with the load capacitor from parallel to series. The optimum number of capacitors and capacitance per boosting capacitor are obtained as a function of the voltage ratio of the required high voltage to the supply voltage. The operation current consumed by the boosting operation is also analytically derived. In addition, another booster circuit-Dickson charge-pump circuit-is optimized under the condition to maximize the output current at a high word-line voltage. Characteristics of the booster circuits are compared, and the selection of booster circuit for low-voltage flash memory is discussed.
Securing Cross-App Interactions in IoT Platforms

IoT platforms enable users to connect various smart devices and online services via reactive apps running on the cloud. These apps, often developed by third-parties, perform simple computations on data triggered by external information sources and actuate the results of computation on external information sinks. Recent research shows that unintended or malicious interactions between the different (even benign) apps of a user can cause severe security and safety risks. These works leverage program analysis techniques to build tools for unveiling unexpected interference across apps for specific use cases. Despite these initial efforts, we are still lacking a semantic framework for understanding interactions between IoT apps. The question of what security policy cross-app interference embodies remains largely unexplored. This paper proposes a semantic framework capturing the essence of cross-app interactions in IoT platforms. The framework generalizes and connects syntactic enforcement mechanisms to bisimulation-based notions of security, thus providing a baseline for formulating soundness criteria of these enforcement mechanisms. Specifically, we present a calculus that models the behavioral semantics of a system of apps executing concurrently, and use it to define desirable semantic policies in the security and safety context of IoT apps. To demonstrate the usefulness of our framework, we define static mechanisms for enforcing cross-app security and safety, and prove them sound with respect to our semantic conditions. Finally, we leverage real-world apps to validate the practical benefits of our policy framework.
Acting Like Humans? Anthropomorphism and Consumer’s Willingness to Pay in Electronic Commerce

Abstract Anthropomorphism is the attribution of human characteristics to a non-human object. Past research shows that anthropomorphism changes how we perceive objects (e.g., believing them to be more attractive). Does this mean we would be willing to pay more for them? We examined whether displaying a product in an anthropomorphized form influenced how much a consumer was willing to pay. We examined two design aspects, visual (i.e., a face) and auditory (e.g., a voice), in the context of an online auction, and proposed three theoretical routes by which an anthropomorphic product display might affect willingness to pay (emotional, product attachment, and product quality). Results show that adding visual anthropomorphizing features to the way a product was displayed increased the amount bid by 7 percent, but adding auditory anthropomorphizing features had no effect. The visual anthropomorphizing features increased product attachment but had no effect on emotions or perceptions of product quality. Therefore, we conclude that anthropomorphizing the way a product is displayed increases willingness to pay primarily through the theoretical route of creating attachment to the product. There is an additional, as yet undiscovered, theoretical route through which anthropomorphism influences willingness to pay. The results also suggest that the conventional wisdom that the combination of visual and auditory design features is best for triggering anthropomorphism is not always true.
Automatic text clustering via particle swarm optimization

An automatic clustering algorithm based on particle swarm optimization, termed ATCPSO, is proposed for texting clustering in this paper. autoPSO has been exploited for evolving the correct number of clusters and simultaneously identifying clusters and has demonstrated to improve the performance of high-dimensional data clustering. To extend autoPSO to text clustering, a few modifications to the algorithm are necessary, such as the similarity measure, parameter selection and the criterion function. Our experimental results on both ten structured text datasets built from 20 newsgroups as well as four text datasets selected from CLUTO show that the proposed algorithm is able to greatly improve the quality of text clustering compared to four typical clustering algorithms and one competitive subspace clustering method.
Diverse Generation for Multi-Agent Sports Games

In this paper, we propose a new generative model for multi-agent trajectory data, focusing on the case of multi-player sports games. Our model leverages graph neural networks (GNNs) and variational recurrent neural networks (VRNNs) to achieve a permutation equivariant model suitable for sports. On two challenging datasets (basketball and soccer), we show that we are able to produce more accurate forecasts than previous methods. We assess accuracy using various metrics, such as log-likelihood and "best of N" loss, based on N different samples of the future. We also measure the distribution of statistics of interest, such as player location or velocity, and show that the distribution induced by our generative model better matches the empirical distribution of the test set. Finally, we show that our model can perform conditional prediction, which lets us answer counterfactual questions such as “how will the players move differently if A passes the ball to B instead of C? ”
A Machine Learning Approach to Building a Tourism Recommendation System using Sentiment Analysis

Opinions have become extremely vital in today’s “ratings” driven technological services. An android application, a top-tier restaurant or any service for that matter thrives or wanes away on the reviews it gets. A good review can help attract potential users while a bad one may drive them away. Thus, it is essential to analyze these reviews to better understand the user’s experience and work towards improving it. The general system that most services use today is based on star-ratings or a score out of 5 or 10. Although these serve the most basic purpose, text-based reviews allow one to understand the reason behind the ratings and are useful to both the user and the service provider to gain more insight. It is impractical for a human to go through thousands of reviews and comprehend the user’s sentiment. Instead, training an algorithm to do this job is much more pragmatic and the advances in machine learning allows one to do so. This is where sentiment analysis comes in. In this paper, analysis of various machine learning algorithms like Multinomial Naïve Bayes, Random Forest Classifier and Bernoulli’s Naïve Bayes has been done and their behavior has been studied. In addition, study
Integrating Orthogonal Localization Methods for Smartphone-based Indoor Localization

In this thesis, we will present an approach of combining several indoor localization mechanisms. Using a weighting function when combining the location estimates provided by the mechanisms we can dynamically decide which information are more useful than others. By that the localization becomes more reliable and resilient to environmental influences and configurations of a single mechanism. The system we defined is extensible and we can easily enhance it by more rules regarding the weighting. We can implement filters easily including fuzzy queries or a preprocessing of the map concerning points of interest or resticted areas for example. Using this system we achieved a mean improvement of the accuracy of more than 30 % and a significant increase of the robustness.
Cache Friendly and Capacity Conscious Scheduling in Multi-core Systems

Current generation high performance multi-core processors have large shared cache memories. This shared cache memory is accessible by multiple cores. Concurrently running threads under each core do not always demand the entire capacity of the shared cache. Threads running on different cores accessing shared cache concurrently may result in higher cache miss rate and significant performance degradation due to inter-thread cache conflicts and lack of cache space. The cache capacity is the quantity of physical cache memory available with the processor. To achieve certain higher degree of processing performance on multi-core processors, efficient shared cache memory usage plays the defining role. The overall processor performance gets more sensitive to the problem of shortage of cache capacity, as threads sharing the cache compete for their requirement of the cache sizes. In this paper, a cache friendly and capacity conscious thread scheduling strategy is proposed for multi-core processors with multiple shared caches. The proposed scheduling policy ensures that the shared cache is optimally used by the competing threads which minimizes inter-thread resource conflict and hence reduces performance degradation. According to the experimental results the proposed policy reduces