Reviewerly/exHarmony huggingface dataset:
Universal Frame Thresholding

We provide the first frame agnostic thresholding scheme based on risk minimization, which can be applied to arbitrary frames and provide its theoretical guarantees. We investigate the proposed scheme, study its empirical risk, and demonstrates how it falls back to the standard Donoho thresholding scheme for orthogonal basis. We then validate our technique and apply it to the overcomplete wavelet transforms of the Deep Scattering Network. We are thus obtaining an invariant and thresholded representation of the signals providing significant performance gains compared to the non-thresholded version.
Functional Asplund metrics for pattern matching, robust to variable lighting conditions

In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund metric which is robust to object absorption variations and ii) the LIP-additive Asplund metric which is robust to variations of source intensity or camera exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess them in various situations with different lightings and movement. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting.
A Novel Multi-Modal Framework for Migrants Integration Based on AI Tools and Digital Companion

ICT have proven to provide significant aid for appropriate integration of migrants. These tools can support the inclusion by providing guidance, education opportunities, job seeking, culture immersion and facilitating access to primary services. In this paper, a complete framework for migrants (with special focus on refugees) guidance and inclusion is presented. This framework comprises a set of novel AI tools aimed at enabling mentioned services from diverse perspectives: a) users’ profiling; b) skills matching c) recommendations; d) user profiling and e) digital companion. Consideration about data collection, data flow, architecture and interactions are provided.
Tensor sparse PCA and face recognition: a novel approach

Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.
Dynamic Property Enforcement in Programmable Data Planes

Network programmers can currently deploy an arbitrary set of protocols in forwarding devices through data plane programming languages such as P4. However, as any other type of software, P4 programs are subject to bugs and misconfigurations. Network verification tools have been proposed as a means of ensuring that the network behaves as expected, but these tools typically require programmers to manually model P4 programs, are limited in terms of the properties they can guarantee and frequently face severe scalability issues. In this paper, we argue for a novel approach to this problem. Rather than statically inspecting a network configuration looking for bugs, we propose to enforce networking properties at runtime. To this end, we developed P4box, a system for deploying runtime monitors in programmable data planes. Our results show that P4box allows programmers to easily express a broad range of properties. Moreover, we demonstrate that runtime monitors represent a small overhead to network devices in terms of latency and resource consumption.
Treats based Revisiting Defences Against Password Guessing Attacks and Phishing Data Over Different Online Records

Password Guessing Attacks, for instance, Brute Force and word reference ambushes on online records are directly wide spread. Guarding the ambushes and giving the accommodating login the genuine customers together is a problematic endeavour. The present structures are lacking to give both the security and solace together. Phishing is a digital assault that targets credulous online clients fooling into uncovering delicate data, for example, username, secret key, standardized savings number or charge card number and so forth. Assailants fool the Internet clients by concealing site page as a dependable or real page to recover individual data. Password Guessing Attacks Resistance Protocol (PGARP) limits the full-scale number of logins attempts from darken remote hosts to as low as a single undertaking for each username, genuine customers all around (e.g., when tries are created utilizing known, occasionally used machines) can make a couple failed login tries before being tried with an ATT. A specific most distant point will be made to oblige the number of failed attempts with the ATT in order to keep the attacks. After the failed login attempt with ATT limit accomplished, an admonition will be sent to the customer concerning the failed login tries have accomplished the best measurement. This admonition will caution the customer and the customer will be urged to change the mystery expression and security question.
INVESTIGATING INTER-RATER RELIABILITY OF QUALITATIVE TEXT ANNOTATIONS IN MACHINE LEARNING DATASETS

Abstract An important step when designers use machine learning models is annotating user generated content. In this study we investigate inter-rater reliability measures of qualitative annotations for supervised learning. We work with previously annotated product reviews from Amazon where phrases related to sustainability are highlighted. We measure inter-rater reliability of the annotations using four variations of Krippendorff's U-alpha. Based on the results we propose suggestions to designers on measuring reliability of qualitative annotations for machine learning datasets.
Compact, Low-cost, Single-frequency NavIC Receiver Development

Global Navigation Satellite Systems are being increasingly used for myriads of applications. Along with many global systems, various regional systems are also deployed to exploit commercial potential, for strategic point of views and self-reliance. IRNSS/ NavIC is one of such regional systems developed by ISRO. NavIC enabled, costly, geodetic receivers are available in market and at the same time, few NavIC enabled chipsets also are being offered by manufacturers. These NavIC chips are low cost but the associated receiver board costs are much higher in comparison to the chipsets. Compact, cost-effective NavIC receiver boards are essential for NavIC based application development, popularization and effective utilization of the system. This manuscript presents an effort towards development of a compact, cost-effective NavIC receiver board and the results on the solution accuracy obtained from it. The power-efficient board operates in L5 band and is of 8 cm  10 cm size. The module is working properly both in static and dynamic operation and can provide meter level 2-dimensional precision; the altitude variation is found to be higher in comparison. Final cost of the prototype board is less than US$ 60 which may be reduced during bulk production. The board can be used with Raspberry Pi (RPi) or other controller boards for ASCII NMEA data reception, display and recording. The board has the potential for using it as a stand-alone receiver, for integration with other systems towards application development or as a geolocation sensor in IoT applications enhancing the possibility of deployment of multiple sensors because of cost, power and size efficiency.
A Lightweight Message Authentication Code for Virtual Work in Future Smart Cities

Data security remains a severe challenge of every IoT setting. As smart devices exchange data and information over the internet, there is a growing concern over securing the integrity and confidentiality of this exchange. Recently the NIST called for submission of proposals for lightweight cryptographic algorithms, to be deployed in IoT devices and other CPU and memory-constrained environment. While many of the submissions are based on existing block cyphers or hash functions, this paper presents a proof-of-concept lightweight MAC uses a Permutation-Quasigroup string transformation as a building block. The string transformation function is initially proposed as a building block for an error detection system in our earlier work. The MAC is implemented and run on a Raspberry Pi B+ single-board computer (SBC), and the result shows that for 3.73%, 3.55% and 2.82% of CPU resources are utilized for hashing messages of size 32 bits, 64 bits and 128 bits respectively. Similarly, the execution time of 0.7secs, 1.02secs and 2.34secs are achieved for hashing messages of size 32 bits, 64 bits and 128 bits respectively. Randomness test was conducted using the NIST statistical test suite, and the result shows that output is sufficiently random. Finally, the MAC is resistant to key exhaustive search attack and is easy to implement.
D <scp>y</scp> VED <scp>eep</scp>

Deep Neural Networks (DNNs) have advanced the state-of-the-art in a variety of machine learning tasks and are deployed in increasing numbers of products and services. However, the computational requirements of training and evaluating large-scale DNNs are growing at a much faster pace than the capabilities of the underlying hardware platforms that they are executed upon. To address this challenge, one promising approach is to exploit the error resilient nature of DNNs by skipping or approximating computations that have negligible impact on classification accuracy. Almost all prior efforts in this direction propose static DNN approximations by either pruning network connections, implementing computations at lower precision, or compressing weights. In this work, we propose &lt;u&gt;Dy&lt;/u&gt;namic &lt;u&gt;V&lt;/u&gt;ariable &lt;u&gt;E&lt;/u&gt;ffort &lt;u&gt;Deep&lt;/u&gt; Neural Networks (D y VED eep ) to reduce the computational requirements of DNNs during inference. Complementary to the aforementioned static approaches, DyVEDeep is a dynamic approach that exploits heterogeneity in the DNN inputs to improve their compute efficiency with comparable classification accuracy and without requiring any re-training. D y VED eep equips DNNs with dynamic effort mechanisms that identify computations critical to classifying a given input and focus computational effort only on the critical computations, while skipping or approximating the rest. We propose three dynamic effort mechanisms that operate at different levels of granularity viz. neuron, feature, and layer levels. We build D y VED eep versions of six popular image recognition benchmarks (CIFAR-10, AlexNet, OverFeat, VGG-16, SqueezeNet, and Deep-Compressed-AlexNet) within the Caffe deep-learning framework. We evaluate D y VED eep on two platforms—a high-performance server with a 2.7 GHz Intel Xeon E5-2680 processor and 128 GB memory, and a low-power Raspberry Pi board with an ARM Cortex A53 processor and 1 GB memory. Across all benchmarks, D y VED eep achieves 2.47×--5.15× reduction in the number of scalar operations, which translates to 1.94×--2.23× and 1.46×--3.46× performance improvement over well-optimized baselines on the Xeon server and the Raspberry Pi, respectively, with comparable classification accuracy.
Authorship attribution of Spanish poems using n-grams and the web as corpus

In many areas of professional development, the categorization of textual objects is of critical importance. A prominent example is the attribution of authorship, where symbolic information is manipulated using natural language processing techniques. In this context, one of the main limitations is the necessity of a large number of pre-labeled instances for each author that is to be identified. This paper proposes a method based on the use of n-grams of characters and the use of the web to enrich the training sets. The proposed method considers the automatic extraction of the unlabeled examples from the Web and its iterative integration into the training data set. The evaluation of the proposed approach was done by using a corpus formed by poems corresponding to 5 contemporary Mexican poets. The results presented allow evaluating the impact of the incorporation of new information into the training set, as well as the role played by the selection of classification attributes using information gain.
Implementation of Building Stability Analyzer with Earthquake Detection Using Simple MEMS Pressure Sensor

Analysis of building stability is an essential measurement process for all buildings in cities for achieving safety and serviceability. This is achieved by measuring the vibration of the building, and also, an alert is created to save the life of people who live in very tall building. It is proposed to design a stability analyzer combined with earthquake detection alarm. When earthquake is detected, the controller checks the vibration level with threshold level. If the vibration input exceeds threshold level, the controller triggers alarm and sends voice message through GSM. In the proposed paper, the MEMS pressure sensor (BMP180) is used to sense the vibration. In addition to sending data to monitoring section, it automatically opens all doors and sends data to nearby rescue station and emergency warning call to evacuate the people from high-rise building. The vibration of the building is recorded continuously for the proper maintenance and service.
BOL<scp>e</scp>R<scp>o</scp>: Behavior optimization and learning for robots

Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art algorithms are often not publicly available though, and experiments are hardly reproducible because open-source implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible to deploy these algorithms on robotic systems. BOLeRo closes this gap for policy search and evolutionary algorithms by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic middlewares and it can be used to compare methods and develop prototypes in simulation.
Semantic Scene Labeling via Deep Nested Level Set

Semantic scene labeling plays a very important role in intelligent transportation tasks, such as autonomous driving and advanced driver assistance. Recently, thanks to the advances of deep learning, significant improvements have been achieved for this pixel-wise labeling task. Although effective, current methods lack of explicitly modeling the boundary of objects, resulting in inaccurate labeling results. Meanwhile, traditional level set based methods perform better to capture the evolution of boundaries. However, they are sensitive to the model initialization. To address these issues, in this work we propose a novel deep learning framework, named deep nested level set (DNLS) for boundary-aware semantic scene labeling. Different from previous works, our proposed framework explicitly takes deep learned features and object boundary information into account. More specifically, our proposed framework first predicts semantic probability maps and boundary locations of objects using a bifurcated fully convolutional network (BFCN). Then, these probability maps are seamlessly integrated into a nested level set function for accurate scene labeling. As a result, our approach can automatically initialize the nested level set function, and the whole framework can be trained in an end-to-end manner, providing a new solution for accurate semantic scene parsing. Extensive experiments on public CamVid and Cityscapes datasets demonstrate that our proposed framework produces high-quality predictions with clear object boundaries and spatial consistency.
Proposal and Comparison of Health Specific Features for the Automatic Assessment of Readability

Looking for health information is one of the most popular activities online. However, the specificity of language on this domain is frequently an obstacle to comprehension, especially for the ones with lower levels of health literacy. For this reason, search engines should consider the readability of health content and, if possible, adapt it to the user behind the search. In this work, we explore methods to assess the readability of health content automatically. We propose features capable of measuring the specificity of a medical text and estimate the knowledge necessary to comprehend it. The features are based on information retrieval metrics and the log-likelihood of a text with lay and medico-scientific language models. To evaluate our methods, we built and used a dataset composed of health articles of Simple English Wikipedia and the respective documents in ordinary Wikipedia. We achieved a maximum accuracy of 88% in binary classifications (easy versus hard-to-read). We found out that the machine learning algorithm does not significantly interfere with performance. We also experimented and compared different features combinations. The features using the values of the log-likelihood of a text with lay and medico-scientific language models perform better than all the others.
Research of Visual Positioning System for Mobile Robots Remote Automatic Control

This paper describe the study for assessing the accuracy of positioning and orientation for a developed visual positioning system, based on the already known methods. The main restrictions for the use of vision systems in the automatic control cycle, including with an increase in the number of observed objects, are revealed. The accuracy of positioning was estimated on the basis of AR tags and color blobs. The authors presented its hybrid marker for speed detection comparable with known analogues. A prototype of the developed mobile robot and the principle of automatic control for motion without collisions in the environment with moving objects are demonstrated. A full cycle of the developed control system with estimates of time delays at each stage is described. As a result, the most acceptable configuration of hardware and software for the task was selected.
Concept Diagrams and the Context Principle

What is the primacy of logic? Concepts, judgments, or inferences? Whereas representationalists traditionally argue for a primacy of the conceptual, rationalists, referring to the context principle and the use theory of meaning, consider judgments and inferences to be primary. This dispute also seems to be applicable to logic diagrams: Whereas “Euler-type diagrams” are actually only for judgments and inferences, “concept diagrams” represent ontologies by using concepts. With reference to Schopenhauer, the paper develops a position called “rational representationalism.” According to this point of view, the question of primacy is decided by analyzing the functions of the logic principles: For the explanation of logic and language, concepts are primary, but for understanding it is judgments. The mediation between intuitive representation and logical rationality is ensured by concept diagrams.
Learning Class Prototypes Via Anisotropic Combination of Aligned Modalities for Few-Shot Learning

Prototypical networks have shown their simplicity and effectiveness in few-shot learning. Recently, some works leverage cross-modal information to enhance the class prototype in few-shot learning. However, they seldom make use of structured information from textual space to optimize class prototype representation. And they either don't align textual modality to visual modality or align them too rigidly. We argue that proper alignment method is important to improve the performance of cross-modal methods, as query data only has visual information in the few-shot learning tasks. In this paper, we propose a cross-modal alignment method to optimize class prototypes with structured information from textual space. We further introduce an anisotropic combination method to enhance class prototypes with information from two modalities. Experiments show that the cross-modal alignment method and anisotropic combination method achieve state-of-the-art results on the miniImageNet and tieredImageNet benchmark in one-shot regime.
Optimized visual recognition algorithm in service robots

Vision-based detection methods often require consideration of the robot’s sight. For example, panoramic images cause image distortion, which negatively affects the target recognition and spatial localization. Furthermore, the original you only look once method does not have a reasonable performance for the image recognition in the panoramic images. Consequently, some failures have been reported so far when implementing the visual recognition on the robot. In the present study, it is intended to optimize the conventional you only look once algorithm and propose the modified you only look once algorithm. Comparing the obtained results with the experiment shows that the modified you only look once method can be effectively applied in the graphics processing unit to reach the panoramic recognition speedup to 32 frames rate per second, which meets the real-time requirements in diverse applications. It is found that the accuracy of the object detection when applying the proposed modified you only look once method exceeds 70% in the studied cases.
THE KARAKURI CARD DECK: CO-DESIGNING INDUSTRIAL IOT CONCEPTUAL SOLUTIONS

Abstract Novel IoT market solutions and research promise IoT modules that do not require programming or electrical setup, yet shop floor personnel need to face problem solving activities to create technical solutions. This paper introduces the Karakuri card deck and presents a case study composed of four workshop sessions in four manufacturing settings, where shop floor personnel tested the cards as a means of ideating and presenting conceptual IoT solutions in the form of diagrams. The results indicate the validity of the proposed conceptual solutions and suggest prototyping as a next step.
Energy production estimation of a parabolic trough solar power plant using artificial neural network

The aim of the present study is the use of Artificial Neural Networks (ANN) modeling to estimate the hourly based electric energy generation of a Parabolic Trough Solar Thermal Power Plant (PTSTPP), located in Eastern Morocco. Data covering 4 years are used in order to train and validate a three Multi-Layers Perceptron (MLP) model. In order to choose the best architecture, several statistical criteria are used such as: Coefficient of Correlation (R), Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Relative Mean Bias Error (RMBE) and Mean Absolute Error (MAE). The back propagation learning algorithm is used to train different ANN architectures. Predicted results indicates that the total electric energy accumulated for the validation year was about 42.6 GWh/year, representing an underestimation less than 5% from the recorded energy. The results indicate that the ANN model can successfully estimate the energy production of a solar power plant with parabolic trough collectors.