Reviewerly/exHarmony huggingface dataset:
A lazy feature selection method for multi-label classification

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.
Remote State Estimation of Nonlinear Systems Over Fading Channels via Recurrent Neural Networks

In this article, we consider the remote state estimation for nonlinear dynamic systems with known linear dynamics and unknown nonlinear perturbations. The nonlinear dynamic plant is monitored by multiple distributed sensors over a random access wireless network with shared common radio channel. We focus on the communication strategy and remote state estimation algorithm design so as to achieve a remote state estimation stability subject to unknown nonlinearities in plant and various wireless impairments, such as multisensor interference, wireless fading, and additive channel noise. By exploiting the additive properties of the physical wireless channels, we propose a novel information fusion over-the-air mechanism to address the signal collision and interference among the sensors. Utilizing the partial knowledge on the linear dynamics of the plant, we also propose a novel recurrent neural network (RNN)-based remote state estimator aided by a virtual state estimation mean-square-error (MSE) process. We further propose a novel online training algorithm such that the RNN at the remote estimator can effectively learn the unknown plant nonlinearities. Using the Lyapunov drift analysis approach, we establish closed-form sufficient requirements on the communication resources needed to achieve almost sure stability of both state estimation and RNN online training in high signal-to-noise ratio (SNR) regime. As a result, our proposed scheme is asymptomatic optimal for large SNR in the sense that both the plant state and the unknown plant nonlinearities can be perfectly recovered at the remote estimator. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.
Victims Can Be Saviors

Micro-architectural side-channel attacks are major threats to the most mathematically sophisticated encryption algorithms. In spite of the fact that there exist several defense techniques, the overhead of implementing the countermeasures remains a matter of concern. A promising strategy is to develop online detection and prevention methods for these attacks. Though some recent studies have devised online prevention mechanisms for some categories of these attacks, still other classes remain undetected. Moreover, to detect these side-channel attacks with minimal False Positives is a challenging effort because of the similarity of their behavior with computationally intensive applications. This article presents a generalized machine learning--based multi-layer detection technique that targets these micro-architectural side-channel attacks, while not restricting its attention only on a single category of attacks. The proposed mechanism gathers low-level system information by profiling performance counter events using Linux perf tool and then applies machine learning techniques to analyze the data. A novel approach using time-series analysis of the data is implemented to find out the correlation of the execution trace of the attack process with the secret key of encryption, which helps in dealing with False-Positives and unknown attacks. This article also provides a detailed theoretical analysis of the detection mechanism of the proposed model along with its security analysis. The experimental results show that the proposed method is superior to the state-of-the-art reported techniques with high detection accuracy, low False Positives, and low implementation overhead while being able to detect before the completion of the attack.
FlashNet: A Real-time Anchor-Free Face Detector

Due to the limitation of computational cost, one of the remaining open challenges in face detection is designing an efficient face detector for mobile platforms. Some of the previous methods obtain superior performance on the WIDER FACE benchmark, but they are computationally unfriendly for mobile devices with limited computing power and memory resource. Inspired by recent anchor-free detection methods, we propose a lightweight anchor-free face detector named FlashNet, which is built with only 150k parameters. Comprehensive and extensive experiments conducted on popular benchmarks show that the proposed lightweight face detector achieves impressive detection accuracy(78.87% AP on WIDER FACE hard validation set and 97.30% TPR discontinuous score on FDDB). Without bells and whistles, the proposed lightweight detector can achieve impressive runtime efficiency that outperforms existing methods by a large margin. Our method achieves 163.93 FPS on NVIDIA TX2 GPU and 30.51 FPS on NVIDIA TX2 CPU at VGA resolution, separately.
Cluster Based Multi Agent System for Breadth First Search

Tree searching problems plays vital role in artificial intelligence and the most of the tree search algorithms have been implemented using structured or object oriented programming. The sequential breadth first search algorithm starts traversing from a source or starting node to neighbour nodes which are adjacently attached to source node in the graphs. It is based on the layer wise exploring the neighbour nodes. The design of multi agent system for searching algorithms is one of the solutions for the algorithm efficiency in graph algorithms. In this paper, the design of multi agent system for breadth first search is presented with implementation detail. The proposed design divides the whole graph into several clusters by using bi-connected region method and assigns the agents to each clusters to perform the breadth first search in each cluster sequentially. The performance measure of the proposed method reveals the competitive results in terms of the sequential breadth first search algorithm.
Efficient Two-Layer Image Protection with Wavelet Transform Compression

The encoding complexity of an image format is a vigorously updating area of study in the field of two-layer protection with wavelet transform compression. In the proposed method, hybrid 2D-FDCT watermarking and RSA encryption for multispectral images predicted an efficient system. This approach satisfies the encryption security, robustness and classification accuracy retention of an algorithm. The two-layer protection of encrypted and embedded watermark image followed by wavelet transform compression minimizes the file size in the exhaustive process for encoding. An important merit is that encoding time is very much reduced in contrast to other security and compression mechanisms. The enhanced value of PSNR as well as trade-off of MES, normalized cross-correlation, the average difference and structural content improves the storage large file size medical image and improves bandwidth to an acceptable level.
Automatic Tagging by Leveraging Visual and Annotated Features in Social Media

Automatic image annotation is one of the research fields helping to extract the meaning of images, which aims at the production of a set of semantic annotations for an image to help better present the concept. Over the past few decades, researchers have developed many approaches for automatic image annotation. Nevertheless, previous studies have not fully accounted for visual features and annotated features. Therefore, it is still possible to achieve a better annotation performance by combining visual and annotated information. In this study, we aim to associate multiple semantic tags with a given image. In particular, we detect how to obtain the image annotation by utilizing visual and annotated information. To take advantage of visual information, we first designed a modified neural network method to acquire the features of the image content. In addition, to obtain the annotated features, we exploit an aggregated network embedding approach that consists of annotation embedding, social embedding, profile embedding, and semantic embedding. Finally, to produce an accurate image annotation, we integrate the two aforementioned methods, that is, combining the visual and annotated information, to build a unified cooperative training framework. The experimental results on three real-world datasets clarify that our presented method is superior to the currently popular image annotation approaches.
Gamayun - Language Technology for Humanitarian Response

Over half of the world's population do not have access to knowledge and information because it's not available in their language. Translators without Borders (TWB) wants to change this with Gamayun, an initiative to promote language equality. Gamayun uses advanced language technologies to improve communication with people who speak marginalized languages in humanitarian and development contexts. In this paper, we present the early implementation results of the project in building machine translation and automatic speech recognition systems for various marginalized languages.
Energy-Based Periodicity Mining With Deep Features for Action Repetition Counting in Unconstrained Videos

Action repetition counting is to estimate the occurrence times of the repetitive motion in one action, which is a relatively new, significant, but challenging problem. To solve this problem, we propose a new method superior to the traditional ways in two aspects, without preprocessing and applicable for arbitrary periodicity actions. Without preprocessing, the proposed model makes our scheme convenient for real applications; processing the arbitrary periodicity action makes our model more suitable for the actual circumstance. In terms of methodology, firstly, we extract action features using ConvNets and then use Principal Component Analysis algorithm to generate the intuitive periodic information from the chaotic high-dimensional features; secondly, we propose an energy-based adaptive feature mode selection scheme to adaptively select proper deep feature mode according to the background of the video; thirdly,we construct the periodic waveform of the action based on the high-energy rules by filtering the irrelevant information. Finally, we detect the peaks to obtain the times of the action repetition. Our work features two-fold: 1) We give a significant insight that features extracted by ConvNets for action recognition can well model the self-similarity periodicity of the repetitive action. 2) A high-energy based periodicity mining rule using features from ConvNets is presented, which can process arbitrary actions without preprocessing. Experimental results show that our method achieves superior or comparable performance on the three benchmark datasets, i.e. YT_Segments, QUVA, and RARV.
Evolutionary aerial robotics: the human way of learning

Robotic aircraft are often required to operate in harsh environments (e.g., underground mining, cluttered environments, and battlefields). In this chapter, we discuss an adaptive (evolving) fuzzy system that has the ability to learn and to configure itself based on the human way of learning, which is also somewhat akin to the principles of natural evolution. We will be looking at the capability of an evolving Takagi-Sugeno (ETS) fuzzy algorithm to learn-from-scratch in order to adapt the challenging dynamics of autonomous systems in real-time. The ETS system can also work in unknown environments, where there is no expert knowledge. While we focus on the implementation of the ETS system to identify the behavior of a fast-dynamical system as in the case of the low altitude hovering of our Tarot hexacopter drone by performing an online ETS-based data driven modelling (online system identification) technique, we also conduct a preliminary study to highlight the efficacy of the ETS autopilot under computer simulations.
VMS Support: A mobile-based support to computerized visitor management system

VMS Support is a mobile-based application which is intended to integrate with a computerized visitor management system (CVMS). The application is used as a receiver of visit notification, respond to the CVMS and update the current whereabout. Visit notification was built using a mobile offline push notification which will not force the organization to invest in any internet provider and subscribe to a premium push notification service. User may respond to visit notification by sending a message that may be visible to the CVMS, and update its current whereabout to guide the visitor correctly.
Casual Gaming to Encourage Reuse of Waste Materials for Environmental Sustainability

Malaysian, especially the younger generation, no longer interested in reusing waste material so that the volume of waste disposal can be reduced. With the intention to create awareness of the importance of reuse practice in Malaysian household, this study proposed and developed ReLife, a casual mobile game that educate gamers about reuse practices. Thirty casual gamers tested the game prototype and the game experience questionnaire was used to collect survey data. The findings support the benefits of persuasive technology, by means of a mobile game, in creating awareness of reuse activity, as well as influencing their attitudes towards the whole environmental issues.
Building a Versatile Knowledge-Based System Based on Reasoning Services and Ontology Representation Transformations

The growing complexity of information systems requires offloading knowledge-based decision making to software. The common approach is to encode formalized domain knowledge in form of ontology. However, this approach has many inherent flaws, such as subjectivity of ontology design, difficulties to formalize different perspectives and points of view for the same domain, complexity and large size of ontologies. This problem can be resolved by using multiple net-worked ontologies. The ultimate criterion for successful ontology design is provided by the effectiveness and efficiency of resolving real life problems using ontology. On the other hand, the efficiency of ontology usage depends also on language used to formulate ontology. Each language is geared towards the specific application area and has limitations which make it a poor choice for problems in another area. Therefore, the versatile knowledge based system should be able change knowledge representation form on task by task basis. The structure and functions of such a system are described. For the central repository of knowledge it is proposed to use a language, based on multisort algebras.
Ananke: A framework for Cloud-Native Applications smart orchestration

Micro-service architecture enables smarter management of applications life-cycle. However, the increasing of the number of components also increases complexity, especially on operations like migration and horizontal scaling. While operations, in monolithic systems, involve only one component, operations in micro-services based applications can get complex and should involve parameters and properties like connection throughput, resources usage, robustness or consistency and reliability. To perform this kind of operations and optimization strategies in micro-services applications, we propose Ananke, a framework consisting of a time-varying multi-layer graph-based model and architecture to profile micro-services and their interactions in a platform-as-a-service environment. The aim of Ananke is to provide support and facilities for optimization strategies that a Cloud Provider can exploit to guarantee quality of service and service-level agreements better.
Merging 3D geological modeling and stochastic simulation to foster waste rock upstream management

Three-dimensional geological modeling is an efficient tool to visualize ore body features during both exploration and operation phases of mines. Repurposing the 3D geological modeling for mine waste management allows for the visualization of hazardous metal(loid)s distribution throughout an ore body and its host rock. With this information, a mine manager could seamlessly carry out waste rocks management based upon their classification. The major prerequisite to such an approach is to procure sufficiently large datasets in order to ensure high interpolation quality and suitable resolution. Apart from metals of economic interest, non-economic and deleterious elements usually do not undergo exhaustive geochemical analyses throughout the footwall and the hanging wall of ore bodies. Based on that premise, the Éléonore mine site provided restricted grades of arsenic, the most hazardous element within the mine setting, to create a 3D spatial model of arsenic content. A stochastic process coupled with the geological logging of drill cores was created to fulfill the 3D modeling prerequisite with known margins of error. The outcome of this work consists of a multi-realization 3D spatial model of arsenic content across the ore deposit and the host rock. Each realization was assessed using available chemical analyses to underline the model's reliability. The results revealed a spacious geochemical halo of arsenic extending up to 500 m away from the gold deposit, with up to 94% of arsenic grades exceeding 50 ppm. The process developed in this work will enable mine waste classification before stripping, thereby providing the opportunity for the formulation of proactive upstream mine waste management options that could prevent future environmental liabilities.
Quantum Communication Networks: Design and Simulation

After having described specific areas of research of applied quantum mechanics such as quantum information theory, quantum error correction, and quantum computing, this chapter leverages those results and contextualizes them in the wider perspective of quantum communication networks. Such networks require the deployment of the so-called quantum repeaters, consisting of quantum and classical hardware performing quantum distillation and storage. Next, the role and characteristics of entanglement generation and distribution are explained, with a specific description of entanglement swapping and quantum network coding. The discussion on quantum communication networks ends with an illustration of quantum multiple-access channel theory and its relation to quantum game theory. Finally, the exposition deals with the problem of simulation and evaluation of quantum communication networks via simulators based on classical hardware and software. Moreover, the chapter lists the main simulators currently available, their characteristics, and their pros and cons.
Person re-identification: Implicitly defining the receptive fields of deep learning classification frameworks

The receptive fields of deep learning models determine the most significant regions of the input data for providing correct decisions. Up to now, the primary way to learn such receptive fields is to train the models upon masked data, which helps the networks to ignore any unwanted regions, but also has two major drawbacks: (1) it yields edge-sensitive decision processes; and (2) it augments considerably the computational cost of the inference phase. Having theses weaknesses in mind, this paper describes a solution for implicitly enhancing the inference of the networks’ receptive fields, by creating synthetic learning data composed of interchanged segments considered apriori important or irrelevant for the network decision. In practice, we use a segmentation module to distinguish between the foreground (important) versus background (irrelevant) parts of each learning instance, and randomly swap segments between image pairs, while keeping the class label exclusively consistent with the label of the segments deemed important. This strategy typically drives the networks to interpret that the identity and clutter descriptions are not correlated. Moreover, the proposed solution has other interesting properties: (1) it is parameter-learning-free; (2) it fully preserves the label information; and (3) it is compatible with the data augmentation techniques typically used. In our empirical evaluation, we considered the person re-identification problem, and the well known RAP, Market1501 and MSMT-V2 datasets for two different settings (upper-body and full-body), having observed highly competitive results over the state-of-the-art. Under a reproducible research paradigm, both the code and the empirical evaluation protocol are available at https://github.com/Ehsan-Yaghoubi/reid-strong-baseline.
A tuned feed-forward deep neural network algorithm for effort estimation

Software effort estimation (SEE) is a software engineering problem that requires robust predictive models. To establish robust models, the most feasible configuration of hyperparameters of regression methods is searched. Although only a few works, which include hyperparameter optimisation (HO), have been done so far for SEE, there is not any comprehensive study including deep learning models. In this study, a feed-forward deep neural network algorithm (FFDNN) is proposed for software effort estimation. The algorithm relies on a binary-search-based method for finding hyperparameters. FFDNN outperforms five comparison algorithms in the experiment that uses two performance parameters. The results of the study suggest that: 1) Employing traditional methods such as grid and random search increases tuning time remarkably. Instead, sophisticated parameter search methods compatible with the structure of regression method should be developed; 2) The performance of SEE is enhanced when associated hyperparameter search method is devised according to the essentials of chosen deep learning approach; 3) Deep learning models achieve in competitive CPU time compared to the tree-based regression methods such as CART_DE8.
Lithological mapping using Landsat 8 Oli multispectral data in Boumalne, Imider, and Sidi Ali Oubork, High Central Atlas, Morocco

The recognition of geological formations, from a lithological point of view, as well as their tectonic character always remain a major challenge for any precise geological study, in this work we will see the great importance of the application of remote sensing (Landsat OLI, DEM) to realize lithological maps of great interest as well as the realization of detailed lineament maps with the statistics of the performed measurements, the application of some indicated techniques showed a good discrimination of the lithology of geological formations (Fig.8 ,Fig.10,Fig.11),better results and interpretations are also obtained for lineaments affecting the whole area by using the CP1 and DEM methods (Fig.5,Fig.6).Topographic map and geological maps of JBEL SAGHRO DADES and TODGHA MADER (1/200000) gained from ministry of energy and mines, direction of geology (1975), are used for validation of the obtained results by eliminating of artificial structures (roads) and geological layer boundaries. All data sets were pre-processed and projected in the Universal Transverse Mercator (UTM Zone 29 North) projection system and the WGS84 datum using GIS tools (ArcGIS 10.2).