Miracle-dz/UniFAR-FTUs huggingface dataset:
Grasp Control with Tactile Feedback

—This work focuses on locally control a robotic hand using tactile feedback in order to achieve a stable grasp. The hand in question belongs to the humanoid robot Vizzy. In the current options for robotic hand interaction with the environment, typically, a centralized architecture is used, in which the primary system directly actuates the hand. Such architectures presents deﬁcits in the speed of communications and, consequently, in the action-reaction grasp control. A local solution is then proposed. This solution consists of a hardware module in Vizzy’s hand dorsal side. The primary system sends grasping commands to the local module, and this one is responsible for controlling the hand. The local module incorporates a layered architecture: high and low level. The high-level layer is responsible for validating and interpreting commands from the primary system and generating trajectories for the low-level to follow. The low-level layer makes the system follow the high-level trajectories and, for that, it applies controllers to the system variables. Independent controllers for the different parts of a complete grasp, contact and non-contact, were developed to build a more efﬁcient low-level controller. Typically, only one variable is used when performing grasping control however, a hybrid controller, gathering force and position, is proposed to actuate the hand during contact. Speed control is the alternative for non-contact control. The complete architecture is implemented in an electromechanical system simulator, developed in Simscape , with the purpose to validate and tune all the development done.
Near-Optimal and Explicit Bell Inequality Violations

Bell inequality violations correspond to behavior of entangled quantum systems that cannot be simulated classically. We give two new two-player games with Bell inequality violations that are stronger, fully explicit, and arguably simpler than earlier work.The first game is based on the Hidden Matching problem of quantum communication complexity, introduced by Bar-Yossef, Jayram, and Kerenidis. This game can be won with probability 1 by a quantum strategy using a maximally entangled state with local dimension n (e.g., log n EPR-pairs), while we show that the winning probability of any classical strategy differs from 1/2 by at most O(log(n)/sqrt(n)). The second game is based on the integrality gap for Unique Games by Khot and Vishnoi and the quantum rounding procedure of Kempe, Regev, and Toner. Here n-dimensional entanglement allows to win the game with probability 1/(log n)^2, while the best winning probability without entanglement is 1/n. This near-linear ratio (``Bell inequality violation'') is near-optimal, both in terms of the local dimension of the entangled state, and in terms of the number of possible outputs of the two players.
Detection Algorithm of Fatal Arrhythmia for a New Implantable Cardioverter Defibrillator using Self-Organizing Map

This article discusses the effectiveness of a new method of detecting fatal arrhythmia used for ICDs. The method utilizes the self-organizing map proposed by Cohonen with an input vector consisting of not only the ECG signal but also the ventricular volume signal to automatically diagnose fatal arrhythmia without setting any thresholds manually. The proposed algorithm was implemented into a single-tip micro computer system and ascertained its effectiveness in an animal experiment using a dog.
Detection of Filament Misalignment in Carbon Fiber Production Using a Stereovision Line Scan Camera System

The performance of carbon fiber semi-finished products as well as all follow-up products is largely dependent on the quality of the carbon fiber rovings. During carbon fiber production various defects affecting quality may occur. Whereas numerous defects such as fuzz balls or roving twists are easy to identify, other fiber fractures remain a challenging defect for inline detection. Such filament fractures are to be recognized during the production process as superficial misaligned filaments. The detection of misalignment however must lie within the scope of a filament width, where a typical carbon filament diameter lies within the order of 5-7 μm in contrast to 2-3 m during fiber mass production. This requires methods of area detection with a resolution in the order of the single filament diameter. Based on the above, the current study focuses on the image processing (described below) and the integration of a camera system along the production line. Due to the required high image resolution, large scan width and current production line speed, image processing as well as the image acquisition requires adapted approaches for the inand online evaluation. To maximize the scan width a stereo vision line scan camera system is included in the production line. The software processes the stereovision camera information for image segmentation from two different angles. As a result, there are two images, a rectified and a disparity image, describing in and out of plane data respectively. The algorithm scans the single lines of the rectified image and transforms the image into a gradient image. The approach searches for connected components that lie within a grayscale range. These areas most likely describe filament misalignment and foreign particles / contaminations. By this we obtain a system able to support the visual inspection by highlighting regions of interest (ROI). With the resolution in the order of a few microns, defects from the filament fractures and small foreign particles up to fuzz balls can be clearly marked. This information forms the basis on which patterns are derived for an automated classification of several defect types. A challenge however remains the statement related to the volumetric amount of defects, since current techniques do not allow the online detection at such required resolution, production speed and width.
Integrating Preferences in Reactive BDI Agents

The belief-desire-intention (BDI) model of agency is based on the notion of reactivity of agents towards internal or external events by selecting, instantiating and executing an applicable goal-plan rule from agent’s procedural knowledge. Current BDI agent-architectures consider the reaction to an event by selecting plans via a “ﬁrst applicable choice” principle. Recent works attempt to include in BDI agents explicit preferences, but do so as a “rationale” thus distorting their reactive nature and modifying the deliberation cycle. This paper proposes a method to include in the agent’s speciﬁcation preferences, and to use them in a manner that preserves reactivity.
Smartphone as low-cost colorimetric sensors

The process of color recognition plays a central role in extracting information from the surrounding environment. Throughout the beginnings of mankind, this process has proved to be a preponderant factor in the quality control of textiles and consumables, as well as in the control of environmental parameters. With the increase in population and consequent industrialization, greater pollution control and environmental monitoring are necessary. However, this type of activity requires the handling of expensive equipment with complex and timeconsuming procedures. The introduction of smartphone and its global success marked a turning point in the development of new environmental monitoring methodologies. Due to their set of sensors and smartphones software can be applied in colorimetric and spectrometric practices. With this objective, a hypothetical solution is proposed for the use of the smartphone as a low cost colorimetric sensor. This work presents the state-of-the-art of the first practices of colorimetry until the inclusion of smartphones in this field. Following this field it is presented the implementation adopted for colorimetric studies using a smartphone. An analysis of the performance of smartphone is made for solutions of various colors and concentrations. The main objective is to test the performance of the implemented solution for the various scenarios simulated by the usage of alimentary dyes and daily life liquids, such as coffee, wine, and fruit juices.
Population genetic structure of the white sardine, Sardinella albella, in the Persian Gulf and Sea of Oman by analysis of mitochondrial control region

Several studies on the white sardine: Sardinella albella, have focused on the identification of stock composition and behavior. In this study population genetic structure and historical demography of S. albella along the cost of the Persian Gulf and Sea of Oman were investigated with a 500-bp segment of mt-DNA control region. In total 40 samples were collected from 3 locations: Jask in Sea of Oman, Qeshm in Strait of Hormuz and Lengeh in the Persian Gulf during 2012-2013. 33 haplotype were obtained none of which were presented in all sampling sites. Analysis of molecular variance (AMOVA) indicated low genetic differentiation among regions (FST=0.024, p<0.05). The average pair wise differences between regional population were small but significant (0.0158-0.165). Molecular variance explained by differences among three regions was significantly different from zero but the FST did not show clear phylogeographic isolation. This observation can support the conclusion that S. albella has a widespread dispersal potential.
UNORDERED FEATURE TRACKING MADE FAST AND EASY

We present an efficient algorithm to fuse two-view correspondences into multi-view consistent tracks. The proposed method relies on the Union-Find [1] algorithm to solve the fusion problem. It is very simple and has a lower computational complexity than other available methods. Our experiments show that it is faster and computes more tracks.
RDBMS support for one-to-many data transformations

Data transformations are essential in the context of data migration, data integration and data warehousing. A data transformation is the fundamental unit of work in each of these processes. One-to-many data transformations [6] constitute a particular class of data transformations that produce several output records for each input record. We describe this class of data transformations, the contexts in which they are useful, and their support by Relational technology. Furthermore, we compare three diﬀerent Relational Database Management Systems (RDBMS) in regard to their performance and expressive power when faced with one-to-many data transformations.
A weakly-supervised discriminative model for audio-to-score alignment

In this paper, we consider a new discriminative approach to the problem of audio-to-score alignment. We consider two distinct informations provided by music scores: (i) an exact ordered list of musical events and (ii) an approximate prior information about relative duration of events. We extend the basic dynamic time warping algorithm to a convex problem that learns optimal classifiers for all events while jointly aligning files, using only weak supervision. We show that the relative duration between events can be easily used as a penalization of our cost function and allows us to drastically improve performances of our approach. We demonstrate the validity of our approach on a large and realistic dataset.
Location-Based Targeting and Ranking for Online Advertising

Contextual advertisement is normally seen as an Information Retrieval problem, where the objective is to retrieve the most relevant ads given a target page. Geotargeting is a particularly interesting specialization of contextual advertising, where the objective is to target ads to audiences concentrated in well-defined areas. Currently, the most common approach involves targeting ads based on the physical location of the visitors, estimated through their IP address. However, there are many situations where it would be more interesting to target ads based on the geographic scope of the target pages, i.e., on the locations referred to in the textual contents of the pages. This work proposes the application of geographic information retrieval techniques to the problem of geotargeting. Specifically, the proposal presented in this work consists of a pipeline of processing stages, which involves (i) determining the geographic scopes of target pages, (ii) classifying target pages according to locational relevance, and (iii) retrieving relevant ads to the target page, using both its textual content and its geographic scope. Experimental results attest for the adequacy of the proposed methods in each of the individual processing stages. The main contribution of this work is to show that it is possible to decompose the geotargeting problem into three separate tasks, which can be independently studied and optimized. Techniques from the area of geographical information retrieval, when applied to each of the considered tasks, can effectively improve the performance of contextual advertisement systems.
The Roasted Tomato Challenge for a Humanoid Robot

Supporting humans in the kitchen is a difficult task for a humanoid robot. This study focuses on one of the tasks identified in the HUMABOT Challenge, finding a tomato in a kitchen environment and grabbing it from the table. Three detection algorithms are evaluated on their performance to find the tomato without confusion with other vegetables present in the kitchen. Once found, the location of the tomato relative to the humanoid robot is estimated. A planning algorithm is then used to grab the tomato from the table with both arms.
Study of Carbohydrate, Antioxidant, Nutrient and Genomic DNA Quantification of Olive Treated with Alkaline and Acidic Solvent: An Innovation

The experiment was conducted to investigate biochemical content, nutritional and DNA characterization of olive fruit treated with alkaline (Sodium chloride anhydrous) and acidic solvent (5% acetic acid, vinegar). The treatments were used as control (no water), water control, 10% sodium chloride anhydrous (NaOH), Vinegar (5% acetic acid), Vinegar + NaOH and Vinegar + NaOH + Hot water treatment. Our results showed that inverted sugar and glucose content were higher in the Vinegar and NaOH treated olive than in other treatments. Fructose content was the highest in Vinegar + NaOH treated fruit. Nutrient contents NO3 K, Ca and Na were found higher in the treated fruit than the control fruit. Moreover, maximum K content was observed in the case of all treatments compared to the other nutrient content. The highest acidic (lower pH) condition (sour) was found in treated fruit. DNA yield was found higher in water control than acid and alkaline treated olives. DNA band was wider in the olive treated water control compared to the NaOH, vinegar, Vinegar + NaOH and Vinegar + NaOH + Hot water treatment. Finally, results suggest that vinegar + NaOH treated olive fruit was the best for fresh olive homemade processing after harvesting for edible purpose.
7.1 ATMOSPHERIC DISPERSION OF ODORS AND GASES FROM ANIMAL WASTES

The rapid development of intensive livestock production systems using confined rearing methods, often close to residential areas, is causing severe environmental problems through the creation of concentrated sources of malodorous animal wastes. Odor problems arise again when the wastes are disposed of by application to agricultural fields. There are at least two needs: predicting concentrations for given source strengths, geometries and weather conditions, needed for planning and hazard assessment, and inferring source strengths from concentrations, needed for regulatory and control purposes.
Soteria: Detecting Adversarial Examples in Control Flow Graph-based Malware Classifiers

Deep learning algorithms have been widely used for security applications, including malware detection and classification. Recent results have shown that those algorithms are vulnerable to adversarial examples, whereby a small perturbation in the input sample may result in misclassification. In this paper, we systematically tackle the problem of adversarial examples detection in the control flow graph (CFG) based classifiers for malware detection using Soteria. Unique to Soteria, we use both density-based and level-based labels for CFG labeling to yield a consistent representation, a random walk-based traversal approach for feature extraction, and n-gram based module for feature representation. End-to-end, Soteria’s representation ensures a simple yet powerful randomization property of the used classification features, making it difficult even for a powerful adversary to launch a successful attack. Soteria also employs a deep learning approach, consisting of an auto-encoder for detecting adversarial examples, and a CNN architecture for detecting and classifying malware samples. We evaluate the performance of Soteria, using a large dataset consisting of 16,814 IoT samples, and demonstrate its superiority in comparison with state-of-the-art approaches. In particular, Soteria yields an accuracy rate of 97.79% for detecting AEs, and 99.91% overall accuracy for classification malware families.
Continuously indexed Potts models on unoriented graphs

This paper introduces an extension to undirected graphical models of the classical continuous time Markov chains. This model can be used to solve a transductive or unsupervised multi-class classification problem at each point of a network defined as a set of nodes connected by segments of different lengths. The classification is performed not only at the nodes, but at every point of the edge connecting two nodes. This is achieved by constructing a Potts process indexed by the continuum of points forming the edges of the graph. We propose a homogeneous parameterization which satisfies Kolmogorov consistency, and show that classical inference and learning algorithms can be applied. We then apply our model to a problem from geomatics, namely that of labelling city blocks automatically with a simple typology of classes (e.g. collective housing) from simple properties of the shape and sizes of buildings of the blocks. Our experiments shows that our model outperform standard MRFs and a discriminative model like logistic regression.
Differentiable Physics Simulation

Differentiable physics simulation is a powerful family of new techniques that applies gradient-based methods to learning and control of physical systems. It enables optimization for control, and can also be integrated into neural network frameworks for performing complex tasks. We believe that differentiable physics simulation should be a key component for neural networks to bridge the gap between training performance and the generality to previously unseen real-world inputs. However, realizing a practical differentiable simulation is still challenging because of its high dimensionality and fragmented computation flow. In this paper, we motivate the importance of differentiable physics simulation, describe its current challenges, introduce state-of-the-art approaches, and discuss potential improvements and future directions.
Cache craftiness for fast multicore key-value storage

We present Masstree, a fast key-value database designed for SMP machines. Masstree keeps all data in memory. Its main data structure is a trie-like concatenation of B+-trees, each of which handles a fixed-length slice of a variable-length key. This structure effectively handles arbitrary-length possiblybinary keys, including keys with long shared prefixes. +-tree fanout was chosen to minimize total DRAM delay when descending the tree and prefetching each tree node. Lookups use optimistic concurrency control, a read-copy-update-like technique, and do not write shared data structures; updates lock only affected nodes. Logging and checkpointing provide consistency and durability. Though some of these ideas appear elsewhere, Masstree is the first to combine them. We discuss design variants and their consequences. On a 16-core machine, with logging enabled and queries arriving over a network, Masstree executes more than six million simple queries per second. This performance is comparable to that of memcached, a non-persistent hash table server, and higher (often much higher) than that of VoltDB, MongoDB, and Redis.
Advanced Visualization for Process Mining (Extended Abstract)

Process mining techniques are used to analyze business processes from event logs. Typically, such techniques generate a model in the form of a graph representing the process behavior extracted from a sequence of events. Depending on this behavior, the graph can be very complex and difﬁcult to analyze. The objective of this work is to create a process mining tool that takes advantage of Web technologies to create interactive models, which allow the user to rearrange the nodes and edges of the graph, ﬁlter elements, etc., in order to facilitate the understanding of these models.
Establishing Multilevel Test-to-Code Traceability Links

Test-to-code traceability links model the relationships between test artefacts and code artefacts. When utilised during the development process, these links help developers to keep test code in sync with tested code, reducing the rate of test failures and missed faults. Test-to-code traceability links can also help developers to maintain an accurate mental model of the system, reducing the risk of architectural degradation when making changes. However, establishing and maintaining these links manually places an extra burden on developers and is error-prone. This paper presents TCtracer, an approach and implementation for the automatic establishment of test-to-code traceability links. Unlike existing work, TCtracer operates at both the method level and the class level, allowing us to establish links between tests and functions, as well as between test classes and tested classes. We improve over existing techniques by combining an ensemble of new and existing techniques and exploiting a synergistic flow of information between the method and class levels. An evaluation of TCtracer using four large, well-studied open source systems demonstrates that, on average, we can establish test-to-function links with a mean average precision (MAP) of 78% and test-class-to-class links with an MAP of 93%.
Intelligent Neighbor Selection in P2P with CapProbe and Vivaldi

—In this paper we propose a new technique for peers on a Pastry network to ﬁnd optimal entries for their routing tables, where a node is optimal if it minimizes ping time, maximizes bandwidth, or attempts to do both. To assist in such neighbor selection, we investigate using Vivaldi in conjunction with CapProbe. A variety of cost functions, or criteria to judge peers, is devised and evaluated through simulation. We ﬁnd that intelligent peer selection, leading to optimized routing table entries, can have beneﬁts of halving end-to-end latency and more than doubling end-to-end throughput.
University of Birmingham High Accuracy Rule-based Question Classification using Question Syntax and Semantics

We present in this paper a purely rule-based system for Question Classiﬁcation which we divide into two parts: The ﬁrst is the extraction of relevant words from a question by use of its structure, and the second is the classiﬁcation of questions based on rules that associate these words to Concepts. We achieve an accuracy of 97.2% , close to a 6 point improvement over the previous State of the Art of 91.6%. Additionally, we believe that machine learning algorithms can be applied on top of this method to further improve accuracy