Miracle-dz/UniFAR-FTUs huggingface dataset:
Average Information based Spectrum Sensing for Cognitive Radio

Wireless Communication resources are power and bandwidth. Hence conservation and proper utilization of spectrum is needed. Spectrum sensing is important since the microwave band is already crowded with many applications. In this paper, a spectrum sensing strategy based on the average information is proposed. The average information is Entropy as per statistical theory of communication. The average information with and without the presence of primary users are calculated using MATLAB. The Entropy when the primary user is not present is H0 hypothesis is found and a threshold is calculated. The Entropy is calculated when the Primary user is present which is H1 Hypothesis. The primary user presence is identified if Entropy for H1 hypothesis is less than the threshold. Histogram is used to calculate the number of samples in each bin. The probability of detection is simulated for various values of SNR. It is found that after an SNR of -8dB, the probability of detection is correct.
CTSD: A Dataset for Traffic Sign Recognition in Complex Real-World Images

Traffic sign recognition (TSR) is an indispensable component for vision-based system of self-driving car. Promising results have been achieved which especially benefit from the rapid development of deep neural networks recently. However, there are few works focusing on the algorithms’ performances towards different complex conditions, such as weather and viewpoint variations. In this paper, we propose a new real-world TSR dataset, which is a dataset with several fine-grained conditions fine labeled involving weather, light condition, occlusion, distance, color fading and camera angle. Detailed and unbiased comparison results are reported about the performances of several state-of-the-arts on our proposed and five public TSR datasets. Experimental results demonstrate that current arts for TSR are still far from satisfactory especially when it comes to complex real-world cases.
A Novel Double Carrier PWM Modulation for Sensorless Control of Permanent Magnet Synchronous Motors

Control of Synchronous Motors at low speed has been the topic of several studies for the last years. Due to the lower amplitude of Back-EMF voltage in lower speed, it is inappropriate to merely rely on induced voltages for position estimation. In this paper, a new double carrier PWM modulation is presented in which two carrier frequencies are utilized for two legs of the inverter. This proposed method rectify the need of powerful processors which decreases the cost of the electric drive system. Employing a non-linear control system and an automatic gain control, ensures the sensorless control of the motor for a wide range of speed.
Microsoft Cambridge at TREC 2002: Filtering Track.

Six runs were submitted for the Adaptive Filtering track, four on the adaptive filtering task (ok11af? ), and two on the routing task (msPUM? ). The adaptive filtering system has been somewhat modified from the one used for TREC–10, largely for efficiency and flexibility reasons; the basic filtering algorithms remain similar to those used in recent TRECs. For the routing task, a completely new system based on perceptrons with uneven margins was used.
Coupled Primary and Secondary Transform for Next Generation Video Coding

The discrete cosine transform type II can efficiently approximate the Karhunen-Loeve transform under the first-order stationary Markov condition. However, the highly dynamic characteristics of natural images will not always follow the first-order stationary Markov condition. It is well known that multi-core transforms and non-separable transforms capture diversified and directional texture patterns more efficiently. And a combination of enhanced multiple transform (EMT) and non-separable secondary transform (NSST) are provided in the reference software of the next generation video coding standard to solve this problem. However, the current method of combining the EMT and NSST may lead to quite significant encoder complexity increase, which makes the video codec rather impractical for real applications. Therefore, in this paper, we investigate the interactions between EMT and NSST, and propose a coupled primary and secondary transform to simplify the combination to obtain a better trade-off between the performance and the encoder complexity. With the proposed method, the transform for the Luma and Chroma components is also unified for a consistent design as an additional benefit. We implement the proposed transform on top of the Next software, which has been proposed for the next generation video coding standard. The experimental results demonstrate that the proposed algorithm can provide significant time reduction while keeping the majority of the performance.
Multi-task Learning for Deep Semantic Hashing

Deep learning to hash has emerged as a popular technique for large-scale image retrieval. Existing deep learning to hash methods seek to solve the single retrieval task within one stream framework or jointly solve the retrieval task and the classification task within two stream framework. Consequently, the semantic information is not fully exploited to generate compact and discriminative hash codes. In this paper, we propose a multi-task learning architecture for deep semantic hashing (MLDH), which incorporates the retrieval task and the classification task within one-stream framework. Specifically, we introduce a COCO loss to learn compact binary codes for the classification task. For the retrieval task, we introduce a pairwise loss to learn discriminative binary codes. Finally, these two tasks are investigated into one-stream deep learning framework. Extensive experiments show that MLDH can outperform state-of-the-art methods on benchmark datasets.
A new High Step-up Three-Port DC-DC Structure for Hybrid PV/Battery Energy Systems

In this paper, a new high step-up three-port structure using one PV panel and a battery as a storage element is proposed. In this proposed structure, three power switches, one single inductor and one coupled inductor are used. The gain of voltage for the presented topology is higher than conventional three-port converters which is referred in this paper. Using one unidirectional and one bidirectional inputs develop the proposed topology as a compatible candidate for hybrid power system. Controlling state of charge (SOC) for battery, and regulating the voltage of output can be achieved by adjusting duty cycles of switches. Also, the capability of supplying the load and either charging or discharging of the storage element is provided by input source. In addition, the capability of supplying the load without PV source is provided by battery. The analysis of steady-state condition in the proposed converter is discussed completely. Finally, the validity of the presented structure is verified by simulation results.
Web-Advertised Generators and Design Wizards

Domain-speciﬁc generators will increasingly rely on Web-based applets for declarative speciﬁca-tions of target applications. Applets will communicate with generators via servers to produce customized code on demand. Critical to the success of this approach will be domain-speciﬁc design wizards , tools that guide users in their selection of components for constructing particular applications. In this paper, we present the P3 ContainerStore applet, its generator, and design wizard.
Level-set random hypersurface models for tracking nonconvex extended objects

This paper presents a novel approach to track a nonconvex shape approximation of an extended target based on noisy point measurements. For this purpose, a novel type of random hypersurface model (RHM) called Level-set RHM is introduced that models the interior of a shape with level-sets of an implicit function. Based on the Level-set RHM, a nonlinear measurement equation can be derived that allows to employ a standard Gaussian state estimator for tracking an extended object even in scenarios with moderate measurement noise. In this paper, shapes are described using polygons, and shape regularization is applied using ideas from active contour models.
Girls rule, boys drool: Extracting semantic and affective stereotypes on Twitter

Social identities carry widely agreed upon meanings, called stereotypes, that have important e ﬀ ects on social processes. We develop a method to extract the stereotypes of a particular population of Twitter users. Our model is grounded in social theory on stereotypes as both identities’ a ﬀ ective meanings and their semantic relationships to each other. We apply our model to a dataset of 45K Twitter users who actively tweeted about the Michael Brown and Eric Garner tragedies. This case study furthers our understanding of both the stereotypes present for those who actively discussed these tragedies online as well as the structure of stereotypes in wider populations both online and o ﬀ .
Perception-based High Dynamic Range Infrared Video Coding

Infrared imagery has been used in many applications. Since the dynamic range of the infrared camera is wider than that of the RGB camera, the infrared image is usually stored in high bit-depth format. This paper proposes a technique to encode the high bit-depth infrared video, where the perception for the high dynamic range content is taken into account. After realizing the modified rate-distortion optimization, the experimental results show that the proposed scheme can achieve up to 13% bitrate reduction while keeping comparable quality in terms of HDR-VDP-2, compared to the H.264/AVC FRExt scheme.
Feature Data Selection for Improving the Performance of Entity Similarity Searches in the Internet of Things

Sensors are used to sense the state information of physical entities in the Internet of Things (IoT). Thus, a large amount of dynamic real-time data is generated. The entity similarity search based on the quantitative dynamic sensor data is thus worth studying. To the best of our knowledge, there is no research on the entity similarity search based on feature data selection for the quantitative dynamic sensor data in the IoT. This paper proposes a selection mechanism for the entity main features (SMEF). The SMEF is a feature data selection method based on the quantitative dynamic sensor data. It uses the feature matrix to delete the irrelevant entity features, applies an improved relief algorithm (iRelief) to calculate the feature data relevance and proposes a three-component storage relation table of the entities, models, and features (TEMF) for the dynamic feature weights calculation. The experimental results show that the similarity search algorithm based on feature data selection can improve the average search accuracy by more than 10%, as well as increase the search speed and reduce the data transmission and storage costs.
Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors

The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient’s emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the “healthcare services package/TAH” of each hospital and the “hospital list”. The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.
Differential Effects of BMI on Brain Response to Odor in Olfactory, Reward and Memory Regions: Evidence from fMRI

Obesity has reached epidemic proportions, motivating research into the underlying mechanisms. Olfaction is a powerful mediator of food consumption, and obesity has been associated with altered olfactory sensitivity. The current study used an event-related functional magnetic resonance imaging (fMRI) to examine the central processing of odor in humans to gain insight into the effect of the body mass index (BMI) on the neural processes involved in rating the pleasantness of a food odor during a hunger state and in a satiety state. We hypothesized that, during the hedonic evaluation of food odor, BMI would be associated with differences in brain activation within olfactory and higher order processing areas important for perception, reward, and memory. We report novel findings of a dissociation between the relationship between BMI and activation in reward areas and in olfactory and odor memory areas, i.e., activation in reward areas decreased as BMI increased, whereas activation in primary olfactory and memory regions increased as BMI increased. A greater BMI is associated with decreased activation in the reward and frontal regions, supporting a blunted reward response in obesity. These findings have important potential implications for decision making, response inhibition, and reward-based behaviors that may play key roles as causal and maintenance factors in obesity. In contrast, a greater BMI is associated with an increased activation in the primary olfactory and memory areas, which was observed during a hunger state. These results raise the speculative hypothesis that high BMI may be associated with hyperactivation in the olfactory and memory areas, and that over time, the resulting excitotoxic effects may contribute to neurodegenerative changes in these areas.
Targeting Brain Disease in MPSII: Preclinical Evaluation of IDS-Loaded PLGA Nanoparticles

Mucopolysaccharidosis type II (MPSII) is a lysosomal storage disorder due to the deficit of the enzyme iduronate 2-sulfatase (IDS), which leads to the accumulation of glycosaminoglycans in most organ-systems, including the brain, and resulting in neurological involvement in about two-thirds of the patients. The main treatment is represented by a weekly infusion of the functional enzyme, which cannot cross the blood-brain barrier and reach the central nervous system. In this study, a tailored nanomedicine approach based on brain-targeted polymeric nanoparticles (g7-NPs), loaded with the therapeutic enzyme, was exploited. Fibroblasts from MPSII patients were treated for 7 days with NPs loaded with the IDS enzyme; an induced IDS activity like the one detected in healthy cells was measured, together with a reduction of GAG content to non-pathological levels. An in vivo short-term study in MPSII mice was performed by weekly administration of g7-NPs-IDS. Biochemical, histological, and immunohistochemical evaluations of liver and brain were performed. The 6-weeks treatment produced a significant reduction of GAG deposits in liver and brain tissues, as well as a reduction of some neurological and inflammatory markers (i.e., LAMP2, CD68, GFAP), highlighting a general improvement of the brain pathology. The g7-NPs-IDS approach allowed a brain-targeted enzyme replacement therapy. Based on these positive results, the future aim will be to optimize NP formulation further to gain a higher efficacy of the proposed approach.
Recommendation Projects at Yahoo

Recommendation is the task of providing content that is likely to interest users and enrich their online experience. At Yahoo! , the variety of user-facing applications and the nature and volume of data raise multiple recommendation opportunities and challenges. In this paper, we provide a brief description of several projects within Yahoo! Labs, in the context of Web Search (Section 2), interpreting users’ clicks and browsing behavior (Section 3), and on the Social Web (Section 4).
Application Modeling using Reverse Engineering Techniques

For the past twenty years, the ACM Symposium on Applied Computing has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. We are recently witnessing an increasing movement in the Software Engineering community towards the use of models for developing software systems. Shifting intellectual property and business logic from source code into models, allows organizations to focus on the important aspects of their systems, which have traditionally been blurred by the usage of standard programming languages and underlying technologies. Model engineering considers models as first-class entities enabling new possibilities for creating, analyzing, and manipulating systems through various types of tools and languages. Each model usually addresses one concern, and the transformations between models provide a chain that enables the automated implementation of a system from its corresponding models. Model transformation definition, implementation and execution become then critical issues of this process. Furthermore, model transformations are also models, and therefore an integral part of this model-based approach. Model transformations need specialized support in several aspects in order to realize their full potential for system modelers, transformation developers, and tool vendors. This goes to the very heart of the OMG's Model Driven Architecture (MDA) initiative, and is an open problem that is being partly addressed by current MOF QVT (Query/View/Transformation) proposals. However, the problem goes beyond having languages to represent model transformations, they also need to be stored in repositories where they can be discovered and reused, they need to be integrated into software development methodologies that make full use of them, need to count on supporting tools and environments, and so forth. SCOPE This track aims at bringing together researchers and practitioners to share experiences in using model transformations and explore the practical problems of existing languages, tools, and environments for transforming models. The track will address questions about the nature and features of model transformations and model merging, requirements of languages for expressing them, measuring the quality and extra-functional requirements of model transformations (e.g., scalability, robustness, adaptability, reusability, etc.), definition of development methodologies that allow exploiting all their potential benefits, etc. TOPICS Topics of interest include, but are not limited to: • Model-driven development methodologies, approaches, and languages • Scalability and reuse of model transformations • Metamodeling • Domain-specific and concern-oriented modelling • Semantics and formal aspects of model transformations • Model Driven Architecture (MDA) and other model-driven approaches …
Deduplication of Audio Files to Remove Redundancy in Cloud storage

With advancement in technology and its ease of availability the use of computing devices is increased. This indeed lead to growth in data. The data generated by these computing devices is called as multimedia data. Multimedia data may include text file, image, audio, video, etc. Amongst the multimedia files, image, audio, video requires large volume of storage than text as the size of those individual files are much higher. There are two existing techniques for eliminating the redundant data in the storage system such as data deduplication and data reduction. Data deduplication is one of the finest method which removes redundancy, reduces the bandwidth and also lowers the disk usage and cost. Studying various research articles, this paper attempts to find the similarity between audio files, achieves redundancy and minimizes the storage cost. Hence, efficient utilization of storage system should be managed properly using deduplication approach for audio files. This paper focus on audio file to be uploaded on cloud and remove the redundancy by using proposed deduplication technique.
Machine Learning Approach for Text Classification in Cybercrime

Nowadays, use of machine learning is increasing rapidly in every field and plays a key role in sentiment classification. In this project, two-training datasets are used, one is an online training dataset which is available online and another contains pure cybercrime data extracted from Facebook and Twitter using Facepager software tool. The aim is to extract the cybercrime data and according to supervised machine learning, separate the data in two labelled class (i.e. positive and negative) and pre-processing it to get a clean training dataset. The goal is to use cybercrime data to achieve classifier accuracy percent and text classification with confidence value that will be achieved by using NLTK and Scikit-learn. The results achieved using both datasets show that using cybercrime datasets gives better classifier accuracy percent.
A Comprehensive Survey on Distributed Transactions Based Data Partitioning

Distributed Transactional systems need to be fast and scalable to increase the performance of the system. Many distributed databases achieve high throughput and scalability through data partitioning. The paper presents comprehensive survey of different techniques and parameters related to distributed transactions used in the Distributed databases. It specifies different ways of partitioning algorithms. The purpose of this review is to study the techniques of horizontal partitioning like range partitioning, schema level partitioning, graph level partitioning etc. giving the high performance and availability of data. The vital aim of these techniques is to reduce distributed transaction and increase scalability of distributed databases.