Miracle-dz/UniFAR-FTUs huggingface dataset:
SURVEY ON AN EFFICIENT APPROACH FOR MULTIMEDIA TRANSMISSION USING MULTIPATH ROUTING IN MANET

Mobile Ad hoc Networks community provides us with a wealth of technologies that enable the source and the destination nodes to route the data through a number of intermediate forwarding nodes. Fast resources discovery and high Quality of Service are key determinants for efficient multimedia transmission. In this paper, we describe a technique of Multipath Routing using AOMDV routing protocol used for multicasting multimedia data transmission in MANET. Multi-path routing represents a promising routing method for wireless mobile ad hoc networks. Multi-path routing achieves load balancing and is more resilient to route failures. Ad Hoc On-demand Multipath Distance Vector protocol is used to choose the multiple paths available for multicasting multimedia data in MANET, based on the rate-distortion metric instead of finding the disjoint paths. The multimedia data further transferred to one and two hop neighbours. The ability of creating multiple routes from the source to a destination is used to provide backup route. When primary route fails to deliver the packets in some way, the backup is used for maintaining connection establishment. Multipath routing using AOMDV achieves lower average end-to-end delay, high video data delivery, lower routing overhead and packet loss rate, higher network throughput, quality of service in comparison with single hop neighbours.
Organización reticular de la memoria semántica. Natural Finder y Definition Finder, dos métodos informatizados para recuperar conocimiento Reticular organization of semantic memory. Natural and Finder Definition finder, two computerized methods for retrieving knowledge

Two types of theories address the organization of conceptual knowledge: holistic and featurebased. The aim of this paper is to present two computerized methods to capture and analyze semantic networks, the Natural Finder and the Definition Finder, which belong to these models respectively. The first is a recursive procedure to capture the natural semantic network of a person around a target concept; the second shows as a result the set of weighted defining features for certain concept given by a social group. Examples of its application are presented.
Avaliação semântica da integração da gestão de riscos de segurança em documentos de software da administração pública

Secure software has its security risks well managed regarding vulnerabilities. IT managers in the Brazilian Federal Public Sector (APF) are legally required to strive for software security. However, only a small number of software security professionals are employed in the technical support for development, maintenance and acquisition of such software in their public organizations. This problem, combined with recent advances in text mining, especially in natural language processing and multi-label classification, motivate this work on research for a computational solution that can understand the semantics of sentences in documents written in Portuguese and connect them to previously defined software security risks, such as OWASP Top Ten. This solution (A2E) can improve the software factories bidding process of the APF by providing the authors and reviewers of technical specifications with a computational tool which can automatically evaluate the integration between security risks management and software processes described in these documents. After applying A2E to more than 120 thousand sentences extracted from OWASP ASVS and past APF specifications, a survey was conducted to compare its performance with the opinion of software engineers and security specialists through objective metrics like Precision, Recall, Hamming Loss and Negative Predictive Values (NPV). A2E’s final version, after a series of improvements in the development process, obtained a significantly better Hamming Loss measure when compared to the specialists’ assessments. Additionally, experiments showed that its NPV is statistically as good as the NPV from the surveyed experts. These results bring interesting new perspectives to future of software security in APF biddings.
Obtenção de padrões sequenciais em data streams atendendo requisitos do Big Data

The growing amount of data produced daily, by both businesses and individuals in the web, increased the demand for analysis and extraction of knowledge of this data. While the last two decades the solution was to store and perform data mining algorithms, currently it has become unviable even to supercomputers. In addition, the requirements of the Big Data age go far beyond the large amount of data to analyze. Response time requirements and complexity of the data acquire more weight in many areas in the real world. New models have been researched and developed, often proposing distributed computing or different ways to handle the data stream mining. Current researches shows that an alternative in the data stream mining is to join a real-time event handling mechanism with a classic mining association rules or sequential patterns algorithms. In this work is shown a data stream mining approach to meet the Big Data response time requirement, linking the event handling mechanism in real time Esper and Incremental Miner of Stretchy Time Sequences (IncMSTS) algorithm. The results show that is possible to take a static data mining algorithm for data stream environment and keep tendency in the patterns, although not possible to continuously read all data coming into the data stream.
Computational model of anthropomorphism for human-robot interaction

Anthropomorphism is one of the most extensively studied topics in HRI. Various factors were reported as affecting the degree to which a robot is perceived as human-like. However, our knowledge of this phenomenon is segmented as there is lack of a comprehensive model of anthropomorphism that could consistently explain these findings. I propose a preliminary model of anthropomorphism that is based on the reverse process to anthropomorphization, known as dehumanization. I hypothesize that the same attributes that people deprive dehumanized others can be used to affect robots anthropomorphism. Since embodiment of a robot can influence what and how these factors can be implemented, the ultimate goal of my research is to propose a method for applying the model of anthropomorphism to different types of robots. Moreover, the impact of interaction stage and context on the model will be explored.
An approach to pricing and resource sharing for available bit rate (ABR) services

We present an approach to pricing and resource sharing for ABR services which allows bursty users to express their true preferences for network usage. Users bid for some amount of effective bandwidth, and the network controls the effective bandwidth of their traffic by adjusting the explicit rate (maximum rate the user can send traffic) in order to achieve economically fair resource sharing. Experiments demonstrate how our approach can differentiate connections with different mean rates, and show that it leads to adequate bandwidth utilization for anticipated link capacities and traffic burstiness.
Review Spam Detection Using Machine Learning Techniques

Nowadays with the increasing popularity of internet, online marketing is going to become more and more popular. This is because, a lot of products and services are easily available online. Hence, reviews about these all products and services are very important for customers as well as organizations. Unfortunately, driven by the will for profit or promotion, fraudsters used to produce fake reviews. These fake reviews written by fraudsters prevent customers and organizations reaching actual conclusions about the products. Hence, fake reviews or review spam must be detected and eliminated so as to prevent deceptive potential customers. In our work, supervised and semi-supervised learning technique have been applied to detect review spam. The most apt data sets in the research area of review spam detection has been used in proposed work. For supervised learning, we try to obtain some feature sets from different automated approaches such as LIWC, POS Tagging, N-gram etc., that can best distinguish the spam and non-spam reviews. Along with these features sentiment analysis, data mining and opinion mining technique have also been applied. For semi-supervised learning, PU-learning algorithm is being used along with six different classifiers (Decision Tree, Naive Bayes, Support Vector Machine, k-Nearest Neighbor, Random Forest, Logistic Regression) to detect review spam from the available data set. Finally, a comparison of proposed technique with some existing review spam detection techniques has been done.
Seeking the trustworthy tweet: Can microblogged data fit the information needs of disaster response and humanitarian relief organizations

Message data has, as yet, not been adopted by large-scale, international humanitarian relief organizations in an instrumental fashion. While the largest of these organizations have adopted messaging as part of their Public Relations functions, few have used any form of message data originating in the field, at the time of disaster. The message data being contributed by bystanders and those affected by a disaster, as it is happening, has largely been deemed as unverifiable and untrustworthy, and thus construed as unsuitable for incorporation into established mechanisms for organizational decision-making. In this paper, we describe the discursive barriers to the use of microblogged data by Humanitarian NGOs during times of disaster. We present data and findings from a study involving representatives from thirteen humanitarian organizations. Our analysis suggests that the organizational barriers, both in terms of function and structure, and the data itself, form barriers to organizational use of microblogged data. We propose three socio-technical solutions to surpassing adoption bottlenecks, namely bounded microblogging, microblogged data as contextual data, and/or use of computational solutions.
Catching an Earworm Through Movement

Earworms, also known as Involuntary Musical Imagery (INMI), are fragments of music that get stuck on mental replay. This study investigates the hypothesis that overt motorinvolvement (in the form of humming, singing, tapping or moving) and imagined motor involvement (in the form of imagining a continuation to an interrupted melody) induces INMI more frequently than passive music listening. Fourgroups of listeners heard one of four songs known to induce earworms in one of four conditions: full song, no movement; song with abrupt midphrase truncation, no movement; full song with instructions to hum, whistle, or sing along; full song with instructions to dance, tap, or nod along. After listening, all participants completed the same monotonous activity, intended to increase earworm induction by setting up a low attention state. Next, participants were asked to report on any earworms that occurred during the session and answer some general questions. Results suggested that vocal and physical involvement trigger INMI more frequently, but the interruption of a tune does not.
Summation and routing on a partitioned optical passive stars network with large group size

In a partitioned optical passive stars (POPS) network, n=dg processors are divided into g groups of d processors each, and such a POPS network is denoted by POPS(d,g). There is an optical passive star (OPS) coupler between every pair of groups. Hence, a POPS(d,g) requires g/sup 2/ couplers. It is likely that, in a practical system, the number of couplers will be less than the number of processors, i.e., d>/spl radic/n>g and the number of groups will be smaller than the number of processors in a group. Hence, it is important to design fast algorithms for basic operations on such POPS networks with large group size. We present fast algorithms for data sum, prefix sum, and permutation routing on a POPS(d,g) such that d>/spl radic/n>g. Our data sum and prefix sum algorithms improve upon the best known algorithms for these problems designed by Sahni (2000). Permutation routing can be solved on a POPS network by simulating a hypercube sorting algorithm. Our algorithm for permutation routing is more efficient compared to this simulated hypercube sorting algorithm.
A New flustering approach for Privacy Preserving Data Fishing in Tele-Health Care Systems

The genesis of the privacy preserving data mining techniques haul out the relevant intellect from mammoth amount of data in Tele-health care systems, while shielding at the same time sensitive information. A number of data fishing techniques, integrating privacy protection mechanisms, have been developed that allow one to smokescreen sensitive item sets or patterns, ahead of the execution of the data fishing process. An imperative issue is to settle on which ones among these privacy preserving techniques are superior enough to protect sensitive information. In this paper, we analyse the existing privacy breaches data perturbation techniques. With the impetus of increasing the data utility, and without compromising the simplicity of the process of perturbation, we have proposed a new variant of the random projection-based perturbation technique for mystifying the tele-health care data of individuals called the flustering approach. We have implemented and evaluated the competence of flustering technique on our own conceptual framework. We hope the proposed solution will get hold of new techniques, paving the way for a research track and working well, according to the evaluation metrics, including hiding effects, data utility and time performance.
PERFORMANCE EVALUATION OF ENERGY EFFICIENT CLUSTERING PROTOCOL FOR CLUSTER HEAD SELECTION IN WIRELESS SENSOR NETWORK

Wireless sensor network are emerging in various fields like environmental monitoring, mining, surveillance system, medical monitoring. LEACH protocol is one of the predominantly used clustering routing protocols in wireless sensor networks. In Leach each node has equal chance to become a cluster head which make the energy dissipated of each node be moderately balanced. We have pioneered an improved algorithm named as Novel Leach based on Leach protocol. The proposed algorithm shows the significant improvement in network lifetime . Comparison of proposed algorithm is done with basic leach in terms of network life time, cluster head selection, energy consumption, and data transmission to base station. The simulation results shows that proposed algorithm can reduce network energy consumption and prolong network life commendably. Simulation of our protocol is done with Matlab.
WIP: Web and education: the effects of the work in "protected environments"

Web surfing, e-mail (electronic mail), chat and still the mere use of a computer, generate in interpersonal communication what we denote as a "protected environment". We have defined protected environment to be the type of communication in which the risk of personal exhibition does not exist. We base this on the fact that it is a very frequent practice in the chat, for example, to use an alias instead of the real name.
SIMULATION AND VERIFICATION OF DIGITAL SYSTEMS WITH NUSMV

We explore, in this work, some possibilities of the new model checker NuSMV, based on model checking algorithms. We started out from the former model checker SMV by verifying a very simple digital device, a two-state MicroWave Oven we designed, and then submitted the same device to the NuSMV Simulator and Model Checker. All results we obtained in verification and simulation with NuSMV are presented in the paper.
Wiktionary for Natural Language Processing: Methodology and Limitations

Wiktionary, a satellite of the Wikipedia initiative, can be seen as a potential resource for Natural Language Processing. It requires however to be processed before being used efficiently as an NLP resource. After describing the relevant aspects of Wiktionary for our purposes, we focus on its structural properties. Then, we describe how we extracted synonymy networks from this resource. We provide an in-depth study of these synonymy networks and compare them to those extracted from traditional resources. Finally, we describe two methods for semi-automatically improving this network by adding missing relations: (i) using a kind of semantic proximity measure; (ii) using translation relations of Wiktionary itself.
Lower Bounds for Error-Correcting Codes with Local Recovery

Error-correcting codes (ECCs) are ubiquitous in computer science. A common property of ECCs is local recovery, which demands that given a corrupted codeword, a single lost code symbol can be recovered by reading only a small part of the codeword. An intriguing problem is to find the most “efficient” ECCs (e.g., codes with short length, codes over a small alphabet) with certain types of local recovery. Both constructions and lower bounds have been proven in the literature. However, the problem is still largely open. In this thesis, we prove three lower bound results on different types of ECCs with local recovery: Firstly, we propose an approximate version of locally decodable codes (LDCs) and prove lower bounds that are similar to the known ones for traditional LDCs. The concerned approximate LDCs are over real numbers and they support recoveries by querying constant number of codeword symbols. The 2-query case (the bulk of our work) is partially related to the lower bound of constant query LDCs, which is a major open problem. Secondly, we generalize the Sylvester-Gallai (SG) theorem to a subspace version. Generally speaking, the setting of the SG theorem is equivalent to 2-query locally correctable codes (LCCs), and our generalization corresponds to the block version of 2-query LCCs. Thirdly, we consider a realistic storage model that is a unification of several families of codes studied in the literature. We prove negative results for codes that attain the maximal recovering capability under this model. Our lower bound rules out the possibility of constructions of efficient codes for most parameter settings. We will also explore some results in the construction direction in the appendix.
Automated Monitoring System for Fall Detection in the Elderly

Falls are a major problem for the elderly people living independently. According to the World Health Organization, falls and sustained injuries are the third cause of chronic disability. In the last years there have been many commercial solutions aimed at automatic and non automatic detection of falls like the social alarm (wrist watch with a button that is activated by the subject in case of a fall event), and the wearable fall detectors that are based on combinations of accelerometers and tilt sensors. Critical problems are associated with those solutions like button is often unreachable after the fall, wearable devices produce many false alarms and old people tend to forget wearing them frequently. To solve these problems, we propose an automated monitoring that will detects the face of the person, extract features such as speed and determines if a human fall has occurred. An alarm is triggered immediately upon detection of a fall.
Towards Mobile Microrobot Swarms for Additive Micromanufacturing

In this paper, a novel approach to achieving the independent control of multiple magnetic microrobots is presented. The approach utilizes a specialized substrate consisting of a fine grid of planar, MEMS-fabricated micro coils of the same size as the microrobots (≤ 500 μm). The coils can be used to generate real magnetic potentials and, therefore, attractive and repulsive forces in the workspace to control the trajectories of the microrobots. Initial work on modelling the coil and microrobot behavior is reported along with simulation results for navigating one and two microrobots along independent desired trajectories. Qualitative results from a scaled-up printed circuit board version of the specialized substrate operating on permanent magnets are presented and offer proof-of-concept results for the approach. These tests also provide insights for practical implementations of such a system, which are similarly reported. The ultimate goal of this work is to use swarms of independently controlled microrobots in advanced, additive manufacturing applications.
Utcai objektumok gyors osztályozása LIDAR pontfelhősorozatokon

Absztrakt. Napjainkban a városi objektumok automatikus osztályozása egy kritikus jelentőséggel bı́ró feladat az önjáró robotok és járművek számı́tógépes érzékeléssel kapcsolatos feladatai közül. Cikkünkben egy új megoldást ajánlunk különböző városi objektumok osztályozására kültéri LIDAR pontfelhősorozatokon. A bemenetként használt háromdimenziós adathalmazt egy Velodyne HDL64 tı́pusú LIDAR lézerszkennerrel készı́tettük különböző városi forgalmi szituációkban. Az általunk készı́tett keretrendszer fogadja a szenzorból érkező pontfelhőfolyamot és célja, hogy lokalizálja és felismerje az adathalmazban elhelyezkedő összes járművet és gyalogost a mozgó szenzor hatósugarában. Első lépésként különböző régiókra szegmentáljuk a szenzorból érkező pontfelhőket a következő osztálycı́mkékkel: 1) talaj 2) alacsony objektumok 3) magas objektumok. Másodsorban, a kinyert 3-D előtérmaszkokon egy újszerű kétrétegű rács struktúra alapú területelárasztásos technikával objektum detekciót hajtunk végre, hogy pontosan becsüljük az egyes objektum kandidánsokhoz tartozó mérési pontokat. A szeparált 3-D objektumokból mélységképet készı́tünk, majd előzetesen az objektum külső megjelenést figyelembe vevő osztályozást hajtunk végre rajtuk egy konvolúciós neurális hálózat (Convolutional Neural Network CNN) segı́tségével. Végül, a városi környezet topológiáját figyelembe véve, kontextuális jellemzőket vezetünk be, hogy finomı́tsuk (javı́tsuk) a kezdeti objektumosztályozást. A kifejlesztett eljárást egy valós városi környezetről készı́tett, 1159 városi objektumot tartalmazó adathalmazon teszteltük, amelyek különböző forgalmi szituációkat foglaltak magukban.
SECURE POLICY EVIDENCE OF USER UPLOADING IMAGES ON SHARING OF INFORMATION SITES

With the expanding volumes of pictures client offers through social destinations, keeping up protection has turned into a noteworthy issue. In light of these episodes, the need of instruments to offer clients control access to their common substance some assistance with being exceedingly key. To offer security for the data, we set forward this paper involving Versatile Protection Strategy Forecast (A3P) structure to offer clients some assistance with creating efforts to establish safety for their pictures. The part of pictures and its metadata are investigated as a measure of client's protection inclinations. The Structure decides the best protection approach for the transferred pictures. It incorporates a Picture grouping system for relationship of pictures with comparable to strategies and a strategy expectation method to naturally deliver a security approach for client transferred pictures.