Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework of Hierarchical Deep Q-Network for Portfolio Management

: Reinforcement Learning algorithms and Neural Networks have diverse applications in many domains, e.g., stock market prediction, facial recognition and automatic machine translation. The concept of modeling the portfolio management through a reinforcement learning formulation is novel, and the Deep Q-Network has been successfully applied to portfolio management recently. However, the model does not take into account of commission fee for transaction. This paper introduces a framework, based on the hierarchical Deep Q-Network, that addresses the issue of zero commission fee by reducing the number of assets assigned to each Deep Q-Network and dividing the total portfolio value into smaller parts. Furthermore, this framework is ﬂexible enough to handle an arbitrary number of assets. In our experiments, the time series of four stocks for three different time periods are used to assess the efﬁcacy of our model. It is found that our hierarchical Deep Q-Network based strategy outperforms ten other strategies, including nine traditional strategies and one reinforcement learning strategy, in proﬁtability as measured by the Cumulative Rate of Return. Moreover, the Sharpe ratio and Max Drawdown metrics both demonstrate that the risk of policy associated with hierarchical Deep Q-Network is the lowest among all ten strategies.
Seam Carving for Image Classification Privacy

: The advent of storing images on cloud platforms has introduced serious privacy concerns. The images are routinely scanned by machine learning algorithms to determine the contents. Usually the scanning is for marketing purposes but more malevolent purposes include criminal activity and government surveillance. The images are automatically analysed by machine learning algorithms. Notably, deep convolutional neural networks perform very well at identifying image classes. Obviously, the images could be encrypted before storing to cloud platforms and then decrypted after downloading. This would certainly obfuscate the images. However, many users prefer to be able to peruse the images on the cloud platform. This creates a difﬁcult problem in which users prefer images stored in a way so that a human can understand them but machine learning algorithms cannot. This paper proposes a novel technique, termed seam doppelganger , for formatting images using seam carving to identify seams for replacement. The approach degrades typical image classiﬁcation performance in order to provide privacy while leaving the image human-understandable. Furthermore, the technique can be largely reversed providing a reasonable facsimile of the original image. Using the ImageNet database for birds, we show how the approach degrades a state-of-the-art residual network (ResNet50) for various amounts of seam replacements.
Comparing Elementary Cellular Automata Classifications with a Convolutional Neural Network

Elementary cellular automata (ECA) are simple dynamic systems which display complex behaviour from simple local interactions. The complex behaviour is apparent in the two-dimensional temporal evolution of a cellular automata, which can be viewed as an image composed of black and white pixels. The visual patterns within these images inspired several ECA classifications, aimed at matching the automatas’ properties to observed patterns, visual or statistical. In this paper, we quantitatively compare 11 ECA classifications. In contrast to the a priori logic behind a classification, we propose an a posteriori evaluation of a classification. The evaluation employs a convolutional neural network, trained to classify each ECA to its assigned class in a classification. The prediction accuracy indicates how well the convolutional neural network is able to learn the underlying classification logic, and reflects how well this classification logic clusters patterns in the temporal evolution. Results show different prediction accuracy (yet all above 85%), three classifications are very well captured by our simple convolutional neural network (accuracy above 99%), although trained on a small extract from the temporal evolution, and with little observations (100 per ECA, evolving 513 cells). In addition, we explain an unreported ”pathological” behaviour in two ECAs.
SLPRNet: A 6D Object Pose Regression Network by Sample Learning

Visual grasping holds important implications for robot manipulation situations. As a core procedure in such grasping tasks, pose regression has attracted lots of research attention, among which point cloud based deep learning methods achieve relatively better result. The usual backbone of such network architectures includes sampling, grouping and feature extracting processes. We argue that common sampling techniques like Farthest Point Sampling(FPS), Random Sampling(RS) and Geometry Sampling(GS) hold potential defectiveness. So we devise a pre-posed network which aims at learning to sample the most suitable points in the whole point cloud for a downstream pose regression task and show its superiority comparing to the above-mentioned sampling methods. In conclusion, we propose a Sample Learning Pose Regression network (SLPRNet) to regress each instances pose in a standard grasping situation. Meanwhile, we build a point cloud dataset to train and test our network. In experiment, we reach an average precision(AP) up to 89.8% on dataset generated from Silane and an average distance(ADD) up to 91.0% on YCB. Real-world grasp experiments also verify the validity of our work.
Automatic Detection and Decryption of AES by Monitoring S-Box Access

In this paper we propose an algorithm that can automatically detect the use of AES and automatically recover both the encryption key and the plaintext. It makes use of the fact that we can monitor accesses to the AES S-Box and deduce the desired data from these accesses; the approach is suitable to software-based AES implementations, both naı̈ve and optimized. To demonstrate the feasibility of this approach we designed a tool which implements the algorithm for Microsoft Windows running on the Intel x86 architecture. The tool has been successfully tested against a set of applications using different cryptographic libraries and common user
Contract Metadata Identification in Czech Scanned Documents

Although nowadays digital-born documents are generally prevalent, exchange of business documents often consists in processing their scanned image form as a general human-readable format with one-to-one correspondence to paper documents. Bulk processing of such scanned documents then requires human intervention to extract and enter the main document metadata. In this paper, we present the design and evaluation of a contract processing module in the OCRMiner system. The information extraction process allows to combine layout properties with text analysis as input to a rule-based extraction with confidence score propagation. The first results are evaluated with public Czech contract documents reaching the item extraction accuracy of
Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets

Studies have shown the vulnerability of machine learning algorithms against adversarial samples in image classification problems in deep neural networks. However, there is a need for performing comprehensive studies of adversarial machine learning in the intrusion detection domain, where current research has been mainly conducted on the widely available KDD’99 and NSL-KDD datasets. In this study, we evaluate the vulnerability of contemporary datasets (in particular, UNSW-NB15 and Bot-IoT datasets) that represent the modern network environment against popular adversarial deep learning attack methods, and assess various machine learning classifiers’ robustness against the generated adversarial samples. Our study shows the feasibility of the attacks for both datasets where adversarial samples successfully decreased the overall detection performance.
An Efficient Low-power Wake-up Receiver Architecture for Power Saving for Transmitter and Receiver Communications

For power-limited wireless sensor networks, energy efficiency is a critical concern. Receiving packages is proven to be one of the most power-consuming tasks in a WSN. To address this problem the asynchronous communication is based on wake-up receivers. The proposed receiver circuit can detect carrier signals inside the 868 MHz band. Reliable signal detection at 10 m was achieved with a total power consumption of 4.2 μW. Two use cases of this low-power receiver were introduced. First the wake-up receiver and second as a collision avoidance circuit. Because of its low power consumption savings of factor 7000 can be estimated compared to integrated solutions of commercially available radio transceivers.
Evaluation of Thermal Resistance of Surface-emitting Quantum Cascade Laser using Structural Function and 3D Thermal Flow Simulation

We analysed the thermal characteristics of a surface-emitting quantum cascade laser (QCL), which is expected to increase output and improve beam quality, on the basis of structural functions and 3D thermal flow simulation. The surface-emitting QCL has a device size of 3 mm x 3 mm and has a photonic crystal for extracting laser light vertically from the QCL. The structural function indicating the heat capacity and thermal resistance of the surface-emitting QCL was measured by the static method, and the total thermal resistance including the mount was about 4.7 K/W. On the other hand, the thermal resistance calculated from the 3D thermal flow simulation of the surface-emitting QCL was 4.55 K/W, showing the results of the two methods to be in good agreement. It was shown that the structural function and the 3D simulation are effective for the thermal analysis of surface-emitting QCLs.
A Novel Al(III)‐Selective Electrochemical Sensor Based on N,N′‐Bis(salicylidene)‐1,2‐phenylenediamine Complexes

A polyvinylchloride membrane sensor based on N,N′-bis(salecylidene)-1,2-phenylenediamine (salophen) as membrane carrier was prepared and investigated as a Al3+-selective electrode. The sensor exhibits a Nernstian response toward Al(III) over a wide concentration range (8.0×10−7–3.0×10−2 M), with a detection limit of 6.0×10−7 M. The potentiometric response of the sensor is independent of the pH of the test solution in the pH range 3.2–4.5. The electrode possesses advantages of very fast response and high selectivity for Al3+ in comparison with alkali, alkaline earth and some heavy metal ions. The sensor was used as an indicator electrode, in the potentiometric titration of aluminum ion and in determination of Al3+ contents in drug, water and waste water samples.
BifurKTM: Approximately Consistent Distributed Transactional Memory for GPUs

We present BifurKTM, the first read-optimized Distributed Transactional Memory system for GPU clusters. The BifurKTM design includes: GPU KoSTM, a new software transactional memory conflict detection scheme that exploits relaxed consistency to increase throughput; and KoDTM, a Distributed Transactional Memory model that combines the Data-and Control-flow models to greatly reduce communication overheads. Despite the allure of huge speedups, GPUs are limited in use due to their programmability and extreme sensitivity to workload characteristics. These become daunting concerns when considering a distributed GPU cluster, wherein a programmer must design algorithms to hide communication latency by exploiting data regularity, high compute intensity, etc. The BifurKTM design allows GPU programmers to exploit a new workload characteristic: the percentage of the workload that is Read-Only (e.g. reads but does not modify shared memory), even when this percentage is not known in advance. Programmers designate transactions that are suitable for Approximate Consistency, in which transactions “appear” to execute at the most convenient time for preventing conflicts. By leveraging Approximate Consistency for Read-Only transactions, the BifurKTM runtime system offers improved performance, application flexibility, and programmability without introducing any errors into shared memory. Our experiments show that Approximate Consistency can improve BkTM performance by up to 34x in applications with moderate network communication utilization and a read-intensive workload. Using Approximate Consistency, BkTM can reduce GPU-to-GPU network communication by 99%, reduce the number of aborts by up to 100%, and achieve an average speedup of 18x over a similarly sized CPU cluster while requiring minimal effort from the programmer.
Representation of PE Files using LSTM Networks

An ever-growing number of malicious attacks on IT infrastructures calls for new and efficient methods of protection. In this paper, we focus on malware detection using the Long Short-Term Memory (LSTM) as a preprocessing tool to increase the classification accuracy of machine learning algorithms. To represent the malicious and benign programs, we used features extracted from files in the PE file format. We created a large dataset on which we performed common feature preparation and feature selection techniques. With the help of various LSTM and Bidirectional LSTM (BLSTM) network architectures, we further transformed the collected features and trained other supervised ML algorithms on both transformed and vanilla datasets. Transformation by deep (4 hidden layers) versions of LSTM and BLSTM networks performed well and decreased the error rate of several state-of-the-art machine learning algorithms significantly. For each machine learning algorithm considered in our experiments, the LSTM-based transformation of the feature space results in decreasing the corresponding error rate by more than 58.60 %, in comparison when the feature space was not transformed using LSTM network.
Strengthening Low-resource Neural Machine Translation through Joint Learning: The Case of Farsi-Spanish

This paper describes a systematic study of an approach to Farsi-Spanish low-resource Neural Machine Translation (NMT) that leverages monolingual data for joint learning of forward and backward translation models. As is standard for NMT systems, the training process begins using two pre-trained translation models that are iteratively updated by decreasing translation costs. In each iteration, either translation model is used to translate monolingual texts from one language to another, to generate synthetic datasets for the other translation model. Two new translation models are then learned from bilingual data along with the synthetic texts. The key distinguishing feature between our approach and standard NMT is an iterative learning process that improves the performance of both translation models, simultaneously producing a higher-quality synthetic training dataset upon each iteration. Our empirical results demonstrate that this approach outperforms baselines.
Post-hoc Explanation using a Mimic Rule for Numerical Data

We propose a novel rule-based explanation method for an arbitrary pre-trained machine learning model. Generally, machine learning models make black-box decisions that are not easy to explain the logical reasons to derive them. Therefore, it is important to develop a tool that gives reasons for the model’s decision. Some studies have tackled the solution of this problem by approximating an explained model with an interpretable model. Although these methods provide logical reasons for a model’s decision, a wrong explanation sometimes occurs. To resolve the issue, we define a rule model for the explanation, called a mimic rule, which behaves similarly in the model in its region. We obtain a mimic rule that can explain the large area of the numerical input space by maximizing the region. Through experimentation, we compare our method to earlier methods. Then we show that our method often improves local fidelity.
Neural Machine Translation for Amharic-English Translation

: This paper describes neural machine translation between orthographically and morphologically divergent languages. Amharic has a rich morphology; it uses the syllabic Ethiopic script. We used a new transliteration technique for Amharic to facilitate vocabulary sharing. To tackle the highly inflectional morphology and to make an open vocabulary translation, we used subwords. Furthermore, the research was conducted on low-data conditions. We used the transformer-based neural machine translation architecture by tuning the hyperparameters for low-data conditions. In the automatic evaluation of the strong baseline, word-based, and subword-based models trained on a public benchmark dataset, the best subword-based models outperform the baseline models by approximately six up to seven BLEU.
Measuring Centers of Special Targets in Digital Still Images and Movie Frames: Approach and Evaluation

Digital cameras are undergoing explosive developments. As a result, high-resolution digital cameras have become very affordable, especially with the latest growth in smart phone technologies. Hence, the usage of existing measuring technologies and the development of new measuring technologies utilizing digital cameras is in high demand. This paper utilizes special black and white patterns, the so-called targets, to measure the relative displacement between two points. The paper introduces a new approach in measuring the location of their centers that is based on best fitting the transition zones to straight lines. The intersection of these lines produces the location of the center with sub-pixel accuracy. A special experimental rig was designed and built to evaluate the approach and compare the measurements to those obtained by a conventional position transducer connected to a data acquisition system. In the first part of the paper, the accuracy of the position transducer and the experimental setup is discussed. It is shown that the accuracy of the setup is much greater than the measurement expected from the digital images. Based on this, it was used as a reference system to evaluate the approach. In the second half of the paper, the approach is evaluated based on monitoring two targets. One of them is fixed and serves as a reference point, whereas another one is floating. The latter can move along the linear bearing system axis, which is orthogonal to the axis of the camera lens. The displacements of the floating target in respect to the fixed target were measured by a position transducer connected to a data acquisition system. The relative displacement of the floating target is captured by the digital camera and is based on the current location of the floating and fixed targets. This paper shows adequate accuracy of the approach and provides recommendations on the ways of keeping it at high accuracy for practical applications in experimental earthquake engineering.
The Comparison of Word Embedding Techniques in RNNs for Vulnerability Detection

Many studies have combined Deep Learning and Natural Language Processing (NLP) techniques in security systems in performing tasks such as bug detection, vulnerability prediction, or classification. Most of these works relied on NLP embedding methods to generate input vectors for the deep learning models. However, there are many existing embedding methods to encode software text files into vectors, and the structures of neural networks are immense and heuristic. This leads to a challenge for the researcher to choose the appropriate combination of embedding techniques and the model structure for training the vulnerability detection classifiers. For this task, we propose a system to investigate the use of four popular word embedding techniques combined with four different recurrent neural networks (RNNs), including both bidirectional RNNs (BRNNs) and unidirectional RNNs. We trained and evaluated the models by using two types of vulnerable function datasets written in C code. Our results showed that the FastText embedding technique combined with BRNNs produced the most efficient detection rate, compared to other combinations, on a real-world but not on an artificially-produced dataset. Further experiments on other datasets are necessary to confirm this result.
Garment Detection in Catwalk Videos

: Most computer vision applications in the commercial scene lack a large scale and properly annotated dataset. The solution to these applications relies on already published code and knowledge transfer from existing computer vision datasets. In most cases, these applications sacriﬁce proper benchmarking of the solution and rely on the performance of used methods from their respective papers. In this paper, we are focusing on how we can use the existing code base and the datasets in computer vision to address a hypothetical application of detecting garments in the catwalk videos. We proposed a combination of methods that allows us to localize garments in complex scenery by only training models on public datasets. To understand which method performs best for our application, we have designed a relative-benchmark framework that requires very little manual annotation to work.
Distributed Framework for Reversible Merging of Heterogeneous Robot Maps

: Studies have shown that multi-robot mapping has the benefit of faster environment exploration when compared to single robot mapping. However, when multiple robots explore the environment simultaneously, a new problem arises – how to merge the individual robot maps. While there are many map merging methods developed for homogeneous maps, heterogeneous robot map merging is still a new research area. Another relatively little researched aspect of map merging is how to deal with an error in the map merging decision. This paper proposes a map merging framework for the distributed merging of heterogeneous robot maps and offers two approaches for the further mapping with an emphasis on map merging process reversibility.
Simulation Framework to Train Intelligent Agents towards an Assisted Driving Power Wheelchair for People with Disability

: Several million people with disabilities exploit power wheelchairs for outdoor mobility on both sidewalks and cycling paths. Especially those with upper limb motor impairments have difﬁculty reacting quickly to obstacles along the way, creating dangerous situations, such as wheelchair crash or rollover. A possible solution could be to equip the power wheelchair with a neural network-based assisted driving system, able to detect, avoid or warn the users of obstacles. Therefore, a virtual environment is required to simulate the system and then test different neural network architectures before mounting the best performing one directly on board. In this work, we present a simulation framework to train multiple artiﬁcial intelligent agents in parallel, by means of reinforcement learning algorithms. The agent shall follow the user’s will and identify obstacles along the path, taking the control of the power wheelchair when the user is making a dangerous driving choice. The developed framework, adapted from an existing autonomous driving simulator, has been used to train and test multiple intelligent agents simultaneously, thanks to a customised synchronisation and memory management mechanism, reducing the overall training time. Preliminary results highlight the suitability of the adapted framework for multiple agent development in the assisted driving scenario.