Miracle-dz/UniFAR-FTUs huggingface dataset:
Versioning Strategy for DevOps Implementations

DevOps is one of the most popular approaches for software delivery nowadays. Even though there is no unified definition of DevOps, there is wide consensus about the set of practices that are part of it. Two of those practices are Infrastructure as Code and Continuous Delivery, which bring new artifacts into the Software Development lifecycle. These new artifacts have direct impact on Software Configuration Management, which is a well-known practice that has been around for decades in the Software Engineering discipline. In particular, these new practices have a direct impact on Version Control. This article describes a Version Control Strategy to manage these new artifacts introduced by DevOps initiatives. The proposed strategy covers the identification of artifacts, versioning tools, version naming conventions and traceability between different artifacts versions. The strategy was validated in three cases of real world projects where it was successfully applied. Each case corresponds to a different kind of organization and in each case a different set of tools where used. Based on these cases, benefits and possible improvements are identified.
Telerehabilitation Platform for Post-arthroplasty Recovery: a Dynamic Time Warping Approach

This study presents key features to develop an efficient tele-rehabilitation platform to support the recovery process in patients after hip replacement surgery. The tool is designed on the concepts of therapeutic education and serious games, in order to enhance the self-rehabilitation. Also, the system is built on a modular architecture to ensure its scalability and adaptation to other physical therapeutic programs. The assessment module integrated in the platform and implemented in dynamic time warping allows for a fair discrimination of the quality of the exercises performed by the users, especially when the motion acquisition is not affected by occlusion. An important contribution of this work is to propose a method that can assess both the range of motion and the compensation movements that may be involved in each exercise.
ToA-based Iterative Localization in Rich Multipath Channels

Iterative localization is arising as a promising solution to determine the position of a mobile station in a cellular network. We recently showed that in a perfect line-of-sight environment, iterating between the conventional delay estimation and multi-lateration steps allows to approach the performance of the direct localization based on the observation of the received signals. In this paper we extend our iterative localization method to operate in rich multipath environments. Simulation results prove that given some prior knowledge on the power delay profile of the channel, the proposed iterative algorithm is robust to harsh propagation environments and performs very close to the direct localization approach.
Spatial Information Extraction using Hidden Correlations

To promote independent living and elderly care in a smart living space, monitoring the activities of daily living and detecting upcoming critical situations are crucial. Thus, exploiting the spatial aspect related to the movement of the resident and their relations to a specific region is important. However, most of existing solutions do not consider related spatial aspect or it is incorporated in a very limited way, in which most solutions need to know the deployment location of sensors. To achieve this, a very time consuming sensor labeling process is needed. In this paper, an unsupervised spatial information extraction technique for smart living space is developed. This technique establishes an abstract spatial representation called virtual location for a physical living space without knowing the actual deployment location of the sensors in that space. A virtual location is achieved by utilizing our extended Louvain modularity method to extract key spatial features from the hidden correlations in the observations perceived through the sensors. By considering the characteristics of different sensors, the effectiveness of virtual location can be further improved. Two real world datasets are used to validate the applicability of the propose technique, and the experimental results for them achieve 90.3% and 71.7% V-measure values respectively.
Successive Convex Approximation Algorithms for Sparse Signal Estimation With Nonconvex Regularizations

In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization–minimization framework and the successive convex approximation framework proposed in literature for a convex regularization function. The proposed framework has several attractive features, namely, first, flexibility, as different choices of the approximate function lead to different types of algorithms; second, fast convergence, as the problem structure can be better exploited by a proper choice of the approximate function and the stepsize is calculated by the line search; third, low complexity, as the approximate function is convex and the line search scheme is carried out over a differentiable function; fourth, guaranteed convergence to a stationary point. We demonstrate these features by two example applications in subspace learning, namely the network anomaly detection problem and the sparse subspace clustering problem. Customizing the proposed framework by adopting the best-response type approximation, we obtain soft-thresholding with exact line search algorithms for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. The attractive features of the proposed algorithms are illustrated numerically.
A Generalized Clustering Method Based on Validity Indices and Membership Functions

Clustering is an important task in data analysis to find a partition on an unlabeled dataset based on similarity relationships among its elements. Typically, such similarity is determined by a proximity measure or distance. Then, the optimal partition is the one that minimizes the distance among elements belonging to the same subset and maximizes the distance among elements from different subsets. The way in which the optimal partition is found is called clustering method. The adequateness of the partition found is commonly determined in terms of a validity index. In this paper, we propose a clustering method referred to as quality-driven search for optimal partition (QDSOC) where the search process of the optimal partition is directly driven by a validity index instead of a proximity measure. Our approach allows to efficiently exploring a large solution space via a breed of genetic algorithm, the so-called eclectic genetic algorithm. Unlike existing clustering methods, the proposed QDSOC offers the optimal partition and provides the mathematical model of such partition in terms of a representation based on membership functions. This model describes the points that belong to the subsets in the partition found. Thus, by using this model, we can predict the membership of new objects without performing the search process again. As part of the experimental evaluation, our proposed QDSOC method is compared with k-means and self-organizing maps (SOMs), which are two well-known clustering approaches. The clustering methods were used to solve a wide sample of clustering problems, and using three different validity indices. From the obtained results, we demonstrate that QDSOC statistically outperforms k-means and SOMs. We also point out that our approach does not incur in excessive computational overhead with respect to such traditional clustering methods.
Tracking and Characterizing the Competition of Fact Checking and Misinformation: Case Studies

Massive amounts of misinformation have been spreading over social media during the 2016 U.S. election season, causing wide public concern about our information ecosystem. How does fact checking compete with misinformation for user attentions? How do misinformation and fact checking differ in their spreading patterns? And what strategies are used by social bots to promote the spread of misinformation? In this paper, we address these research questions by analyzing datasets collected by Hoaxy. We answer the first question by conducting: 1) a survival analysis shows that about 70% of claims will be fact-checked in one week and about 800 tweets with claim links are posted during this period, on average and 2) a cross-correlation analysis shows that the sharing of fact-checking articles typically lags that of misinformation by about one day. Regarding the second question, we answer it from three points of view: 1) we find that false claims can become more popular than the corresponding debunking; 2) when looking at the distribution of types of tweets, we find that fact checking tends to spread in a conversational way; and 3) bot behavior analysis shows that the most active accounts sharing misinformation behave more like social bots. To better understand how social bots spread misinformation, we conduct case studies to answer the third question. By presenting strategies such as the production of a large number of original tweets, the alternating and hijacking of hashtags, and the injection of content into conversations, we demonstrate how social bots take advantages of the recommendation features of Twitter to amplify the spread of misinformation.
The Role of Distance Educational Technologies in the Development of Educational Programmes in a Network Form: Experience and Relevant Problems

Modern Russian legislation presupposes the development of a new educational market sector of education programs based on network cooperation between universities. Organization technologies of the educational process are based on contract relations and a possibility of using a modern electronic educational environment and distance technologies. Practical implementation of network programs includes mutually beneficial terms of cooperation and the young people's need to gain access to new developments of leading scientific schools within universities.
A Rectifier-Less AC–DC Interface Circuit for Ambient Energy Harvesting From Low-Voltage Piezoelectric Transducer Array

This paper presents a rectifier-less ac–dc energy harvesting circuit capable of harvesting energy from multiple low-voltage piezoelectric transducers (PETs). Synchronous electric charge extraction technique, with bidirectional switches, is adopted to achieve rectifier-less ac–dc direct conversion. The inductor is engaged only during the voltage peak of the PET output for a short period of time and therefore, a single inductor can be shared by multiple transducers. A split-capacitor charging topology is employed to harvest both positive and negative half-cycle energies, without the use of an input rectifier. In addition, a self-startup function is incorporated to kick-start the system from low input voltages. A prototype has been implemented with off-the-shelf components. Energy harvesting from three PET energy sources with different resonance frequencies is demonstrated. A peak overall power conversion efficiency of 79% is achieved with a system self-startup voltage of 650 mV.
Some Reflections on the Potential and Limitations of Deep Learning for Automated Music Generation

Deep Learning and Artificial Intelligence are slowly revolutionizing many fields of applications, having the potential to replace humans in a variety of tasks and jobs. Nevertheless, creativity has always been considered something inherently human: recent research shows that, however, this may not always be the case. From this standpoint, this paper focuses on music and on the recent advancements in deep learning applied to the generation of musical content. We argue that, while those models are able to produce results that could actually be considered music, the role of the human musician still remains preponderant in the production of a musical piece. We here reflect on such limitations, directing our efforts to imagining new tools and instruments that may allow to experience new forms of interaction while supporting novel processes of creativity and music production.
A 10-Bit 600-MS/s Time-Interleaved SAR ADC With Interpolation-Based Timing Skew Calibration

This brief presents a 10-bit 600 MS/s 4-channel time-interleaved (TI) successive approximation register analog-to-digital converter (ADC). A background calibration algorithm using Lagrange polynomial interpolation is introduced to calibrate timing skew. It consists of digital detection and adaptive derivative-based correction, employing low filter taps and resulting in hardware reduction. Two reference voltage generators are implemented on-chip to provide a stable reference voltage for the sub-ADCs, enhancing the reliability and robustness of the circuits. The TI-ADC prototype is fabricated in the 65-nm CMOS process and occupies an area of 0.69 mm2. The measurement results show that at a sampling rate of 600 MS/s the ADC achieves a 49-dB SNDR after calibration while dissipating 34 mW from a 1.2/2.5-V supply.
Towards Robust Human-Robot Collaborative Manufacturing: Multimodal Fusion

Intuitive and robust multimodal robot control is the key toward human–robot collaboration (HRC) for manufacturing systems. Multimodal robot control methods were introduced in previous studies. The methods allow human operators to control robot intuitively without programming brand-specific code. However, most of the multimodal robot control methods are unreliable because the feature representations are not shared across multiple modalities. To target this problem, a deep learning-based multimodal fusion architecture is proposed in this paper for robust multimodal HRC manufacturing systems. The proposed architecture consists of three modalities: speech command, hand motion, and body motion. Three unimodal models are first trained to extract features, which are further fused for representation sharing. Experiments show that the proposed multimodal fusion model outperforms the three unimodal models. This paper indicates a great potential to apply the proposed multimodal fusion architecture to robust HRC manufacturing systems.
MV-PURE Spatial Filters With Application to EEG/MEG Source Reconstruction

In this paper, we propose spatial filters for a linear regression model, which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a sample application, we consider the problem of reconstruction of brain activity from electroencephalographic (EEG) or magnetoencephalographic (MEG) measurements. The proposed filters come in two versions depending on whether or not the EEG/MEG forward model explicitly considers interfering activity in the way of brain activity originating in regions different to those of main interest, but measured as correlated with signals of interest by the EEG/MEG sensor array. In both cases, the proposed filters are equipped with a rank-selection criterion minimizing the mean-square error (MSE) of the filter output. Therefore, we consider them as novel nontrivial generalizations of well-known linearly constrained minimum variance and nulling filters. In order to facilitate reproducibility of our research, we provide (jointly with this paper) comprehensive simulation framework that allows for estimation of error of signal reconstruction for a number of spatial filters applied to MEG or EEG signals. Based on this framework, chief properties of proposed filters are verified in a set of detailed simulations.
A Novel Structure of Convolutional Layers with a Higher Performance-Complexity Ratio for Semantic Segmentation

In this paper, we study an important factor that determines the capacity of a CNN model and propose a novel structure of convolutional layers with a higher performance-complexity ratio. Firstly, the relationship of the model capacity and the number of parameters versus segmentation performance is explored. Secondly, a mechanism is proposed to optimize the structure of a CNN model for a specific task. The mechanism also provides better convergence than current state-of-the-art methods for factorizing convolutional layers, such as MobileNet. Thirdly, we propose a measure based on the mutual information between hidden activations and inputs/outputs to compute the capacity of a CNN model. This measure is highly correlated with segmentation performance. Experimental results on the segmentation of the PASCAL Person Parts Dataset show that the linear dependency among convolutional kernels is an important factor determining the capacity of a CNN model. It is also demonstrated that our approach can successfully adjust the model capacity to best match to the complexity of a dataset. The optimized CNN model achieves the similar performance to Deeplab-V2 on the segmentation task with 100 × less parameters, resulting in a significantly improved performance-complexity ratio.
Genetic Associations on Diagnosis of Parkinson's Disease with Non-Genetic Multimodality Tests Based on Feature Ranking

As studies have found evidence of genetic associations to Parkinson's disease (PD), this paper investigates genetic associations on diagnosis of PD using conventional multimodality PD clinical tests instead of expensive genetic sequencing studies. An importance-driven approach is used to rank features from multimodality clinical tests to determine few key features out of the pool of features. Statistical significance tests are then conducted to validate ranked key features for differentiating genetic PD from other studied categories. The ranked key features are input into various machine learning models for the classification of different PD related categories (PD, GENPD, GENUN). The experiment results show that conventional PD clinical tests without expensive genetic sequencing studies can successfully identify genetic associations to PD from other studied categories.
Co-Optimizing Storage Space Utilization and Performance for Key-Value Solid State Drives

Growing demand for key-value store applications is building a strong momentum for the commercialization of key-value hard disk drives. To achieve better performance, flash-based solid state drive is the next ideal candidate for commercialization in the foreseeable future. However, the existing fixed-sized management strategies of flash-based devices would potentially result in low storage space utilization when managing variable-sized key-value data. In addition, the low storage space utilization would further lead to the degradation of device performance, due to low invalid data space reclamation efficiency. The space utilization issue motivates this paper to propose a key-value flash translation layer design to improve storage space utilization as well as the performance of the key-value solid state drives. A series of experiments was conducted to evaluate the proposed design, and the experiment results of space utilization and device performance are very encouraging.
A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.
Classification of Heterogenous M2M/IoT Traffic Based on C-plane and U-plane Data

This paper is motivated by the observation that M2M/IoT traffic is rather heterogeneous. By using traffic data collected in a real scenario, we prove that some M2M devices might generate a signaling traffic more similar to the traffic of a smartphone than to the traffic of a traditional M2M device used for metering applications. This makes difficult the task of a mobile operator to understand the impact of the introduction of M2M/IoT devices into the network. The paper presents a classification of the M2M/IoT heterogeneous world in three classes. The classification has been performed using the C-plane attributes and its effectiveness has been assessed by performing a clustering analysis over the U-plane data, which represent the ground truth. We found a good matching between the results of the classification task carried out by using the C-plane data and the results of the clustering task carried out by exploiting the U-plane data. This means that, by using the C-plane data (simpler with respect to using U-plane data) the network operator can understand with a good accuracy which type of M2M devices are active on the network, and what are the applications that they run and the data traffic that they generate. Therefore, the results may be useful for a proper dimensioning and management of the evolution of an EPC network.
Activity Recognition Based on RGB-D and Thermal Sensors for Socially Assistive Robots

For socially assistive robots, being able to recognize basic human actions is an important capability. The sensors, which are frequently mounted on most recent robots, such as RGB-D and thermal cameras, as well as the advances in deep learning have enabled the research on activity recognition to grow. In this paper, we collected our own dataset of actions in a home-like scenario, which contains thermal imagery in addition to RGB-D data and we proposed a method based on Long-term Recurrent Convolutional Networks (LRCN). We showed that our method has an accuracy comparable with the state-of-the-art. We also proved that thermal information can improve the recognition accuracy. Furthermore, we tested the real-time capability of our system and conducted a real-time experiment with a robot (Pepper robot from Softbank Robotics) so as to investigate the effect of a robot enabled with action recognition capability in a human-robot interaction.
Active Learning in Handwritten Text Recognition using the Derivational Entropy

Handwritten Text Recognition systems are based on statistical models such as recurrent neural networks or hidden Markov models for optical modeling of characters. These models need large corpora for training, consisting in text line images with their corresponding transcripts. The manual annotation of this training data is expensive because it is carried out by experts in paleography, who are specialized in reading ancient scripts. An alternative to reduce the annotation human effort is to use Active Learning techniques to selecting the most informative samples to be used for training. In this paper we study an Active Learning technique to selecting the most informative samples in an HTR scenario. The expert paleographer transcribes only the most informative samples in each stage. The technique followed here is based in the derivational entropy computed from word-graphs obtained from the recognition process.