Reviewerly/exHarmony huggingface dataset:
Joint Transfer of Model Knowledge and Fairness Over Domains Using Wasserstein Distance

Owing to the increasing use of machine learning in our daily lives, the problem of fairness has recently become an important topic in machine learning societies. Recent studies regarding fairness in machine learning have been conducted to attempt to ensure statistical independence between individual model predictions and designated sensitive attributes. However, in reality, cases exist in which the sensitive variables of data used for learning models differ from the data upon which the model is applied. In this paper, we investigate a methodology for developing a fair classification model for data with limited or no labels, by transferring knowledge from another data domain where information is fully available. This is done by controlling the Wasserstein distances between relevant distributions. Subsequently, we obtain a fair model that could be successfully applied to two datasets with different sensitive attributes. We present theoretical results validating that our approach provably transfers both classification performance and fairness over domains. Experimental results show that our method does indeed promote fairness for the target domain, while retaining reasonable classification accuracy, and that it often outperforms comparative models in terms of joint fairness.
On Connections Between Regularizations for Improving DNN Robustness

This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view. Specifically, we study possible connections between several effective methods, including input-gradient regularization, Jacobian regularization, curvature regularization, and a cross-Lipschitz functional. We investigate them on DNNs with general rectified linear activations, which constitute one of the most prevalent families of models for image classification and a host of other machine learning applications. We shed light on essential ingredients of these regularizations and re-interpret their functionality. Through the lens of our study, more principled and efficient regularizations can possibly be invented in the near future.
Fault diagnosis model based on Granular Computing and Echo State Network

In order to improve the efficiency and accuracy of electronic equipment fault diagnosis, a fault diagnosis model based on Granular Computing and Echo State Network (ESN) is proposed. Firstly, the attribute reduction of test index is carried out based on granular computing model. An attribute distinguishing ability index is defined based on attribute value influence degree. As the basis of similarity measure, a number of attribute granules of similar distinguish are obtained through affinity propagation clustering algorithm, then fault attribute reduction was completed by selecting clustering center attributes. In the stage of fault identification by ESN, in order to improve the dynamic adaptability of ESN reservoir to samples, Bienenstock–Cooper–Munro(BCM) rule is introduced into the reservoir construction to train the connection weight matrix. Meanwhile, the L1∕2-norm penalty term is added to the objective function in order to improve the sparsification efficiency, and a smoothing L1∕2-norm regularization term is used to overcome the iterative numerical oscillation problem, the model is solved by using the half threshold iteration method at last. The effectiveness and superiority of the proposed method are verified by a fault diagnosis example of terminal guidance radar signal processing module.
Multi-layer heterogeneous ensemble with classifier and feature selection

Deep Neural Networks have achieved many successes when applying to visual, text, and speech information in various domains. The crucial reasons behind these successes are the multi-layer architecture and the in-model feature transformation of deep learning models. These design principles have inspired other sub-fields of machine learning including ensemble learning. In recent years, there are some deep homogenous ensemble models introduced with a large number of classifiers in each layer. These models, thus, require a costly computational classification. Moreover, the existing deep ensemble models use all classifiers including unnecessary ones which can reduce the predictive accuracy of the ensemble. In this study, we propose a multi-layer ensemble learning framework called MUlti-Layer heterogeneous Ensemble System (MULES) to solve the classification problem. The proposed system works with a small number of heterogeneous classifiers to obtain ensemble diversity, therefore being efficiency in resource usage. We also propose an Evolutionary Algorithm-based selection method to select the subset of suitable classifiers and features at each layer to enhance the predictive performance of MULES. The selection method uses NSGA-II algorithm to optimize two objectives concerning classification accuracy and ensemble diversity. Experiments on 33 datasets confirm that MULES is better than a number of well-known benchmark algorithms.
SofTEE: Software-Based Trusted Execution Environment for User Applications

Commodity operating systems are considered vulnerable.Therefore, when an application handles security-sensitive data, it is highly recommended to run the application in a trusted execution environment.In response to this demand, hardware-based trusted execution environments such as Intel SGX and ARM TrustZone have been developed in commodity computers.However, hardware-based approaches cannot be quickly upgraded to address design vulnerabilities or to reflect customer feedback.In this paper, we propose SofTEE, a software framework to support a trusted execution environment for user applications.For a trusted execution environment, SofTEE should support memory isolation and attestation.For memory isolation, SofTEE relies on kernel deprivileging which delegates the execution of privileged operations such as memory management, from a kernel to a special module called a security monitor.To reduce the overhead of switching between the deprivileged kernel and the security monitor, SofTEE proposes an efficient management mechanism of the address space identifier.SofTEE supports attestation by assuming minimal hardware functionalities of random entropy and root of trust.The main challenge of SofTEE is to guarantee security properties like confidentiality and integrity of security-sensitive applications.For security analysis, we have identified security invariants that SofTEE should meet for confidentiality and integrity guarantees.Based on the security invariants, we have designed and prototyped each component of SofTEE on a Raspberry Pi 3 board.SofTEE produces about 3% overhead in case of a security-sensitive application with long execution time and 23% overhead in case of a security-sensitive application with short execution time.
Cooperative Multi-Agent Reinforcement Learning With Approximate Model Learning

In multi-agent reinforcement learning, it is essential for agents to learn communication protocol to optimize collaboration policies and to solve unstable learning problems. Existing methods based on actor-critic networks solve the communication problem among agents. However, these methods have difficulty in improving sample efficiency and learning robust policies because it is not easy to understand the dynamics and nonstationary of the environment as the policies of other agents change. We propose a method for learning cooperative policies in multi-agent environments by considering the communications among agents. The proposed method consists of recurrent neural network-based actor-critic networks and deterministic policy gradients to centrally train decentralized policies. The actor networks cause the agents to communicate using forward and backward paths and to determine subsequent actions. The critic network helps to train the actor networks by sending gradient signals to the actors according to their contribution to the global reward. To address issues with partial observability and unstable learning, we propose using auxiliary prediction networks to approximate state transitions and the reward function. We used multi-agent environments to demonstrate the usefulness and superiority of the proposed method by comparing it with existing multi-agent reinforcement learning methods, in terms of both learning efficiency and goal achievements in the test phase. The results demonstrate that the proposed method outperformed other alternatives.
Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian

The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on the text compression and provide a comparison of algorithms (in particular, entropy-based arithmetic and dictionary-based Lempel–Ziv–Welch (LZW) methods) for text compression in different languages (Croatian, Finnish, Hungarian, Czech, Italian, French, German, and English). The main goal is to answer a question: ”How does the language of a text affect the compression ratio?” The results indicated that the compression ratio is affected by the size of the language alphabet, and size or type of the text. For example, The European Green Deal was compressed by 75.79%, 76.17%, 77.33%, 76.84%, 73.25%, 74.63%, 75.14%, and 74.51% using the LZW algorithm, and by 72.54%, 71.47%, 72.87%, 73.43%, 69.62%, 69.94%, 72.42% and 72% using the arithmetic algorithm for the English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian versions, respectively.
Efficient Hardware Architectures for 1D- and MD-LSTM Networks

Abstract Recurrent Neural Networks, in particular One-dimensional and Multidimensional Long Short-Term Memory (1D-LSTM and MD-LSTM) have achieved state-of-the-art classification accuracy in many applications such as machine translation, image caption generation, handwritten text recognition, medical imaging and many more. However, high classification accuracy comes at high compute, storage, and memory bandwidth requirements, which make their deployment challenging, especially for energy-constrained platforms such as portable devices. In comparison to CNNs, not so many investigations exist on efficient hardware implementations for 1D-LSTM especially under energy constraints, and there is no research publication on hardware architecture for MD-LSTM. In this article, we present two novel architectures for LSTM inference: a hardware architecture for MD-LSTM, and a DRAM-based Processing-in-Memory (DRAM-PIM) hardware architecture for 1D-LSTM. We present for the first time a hardware architecture for MD-LSTM, and show a trade-off analysis for accuracy and hardware cost for various precisions. We implement the new architecture as an FPGA-based accelerator that outperforms NVIDIA K80 GPU implementation in terms of runtime by up to 84× and energy efficiency by up to 1238× for a challenging dataset for historical document image binarization from DIBCO 2017 contest, and a well known MNIST dataset for handwritten digits recognition. Our accelerator demonstrates highest accuracy and comparable throughput in comparison to state-of-the-art FPGA-based implementations of multilayer perceptron for MNIST dataset. Furthermore, we present a new DRAM-PIM architecture for 1D-LSTM targeting energy efficient compute platforms such as portable devices. The DRAM-PIM architecture integrates the computation units in a close proximity to the DRAM cells in order to maximize the data parallelism and energy efficiency. The proposed DRAM-PIM design is 16.19 × more energy efficient as compared to FPGA implementation. The total chip area overhead of this design is 18 % compared to a commodity 8 Gb DRAM chip. Our experiments show that the DRAM-PIM implementation delivers a throughput of 1309.16 GOp/s for an optical character recognition application.
A color image steganography method based on ADPVD and HOG techniques

Steganography is an important secret information communication technology in which one may send secret data without others having knowledge of its existence. This chapter proposes a pixel-based adaptive directional pixel value differencing (P-ADPVD) method. The original pixel value differencing (PVD) uses only one embedding direction for all pixels of the cover image, whereas the content of the digital images has different edge direction for each pixel. Therefore it is better to exploit this feature for embedding secret data in various edge directions. To this end, the proposed P-ADPVD method depends on a pixel-of-interest (POI) to hide secret data bits in the dominant edge direction for each pixel. Histogram of oriented gradient (HOG) algorithm is employed to find the dominant edge direction for each block of the POIs using gradient magnitude and angle information calculated from the cover image and determined by a threshold value. This methodology significantly improves the quality and security of the stegoimages without sacrificing the embedding capacity. Finally, the experimental results using various digital images prove that the P-ADPVD method provides better visual quality of the stegoimage compared with other adaptive and nonadaptive PVD-based methods.
Spatio-Temporal VLAD Encoding of Visual Events Using Temporal Ordering of the Mid-Level Deep Semantics

Classification of video events based on frame-level descriptors is a common approach to video recognition. In the meanwhile, proper encoding of the frame-level descriptors is vital to the whole event classification procedure. While there are some pretty efficient video descriptor encoding methods, temporal ordering of the descriptors is often ignored in these encoding algorithms. In this paper, we show that by taking into account the temporal inter-frame dependencies and tracking the chronological order of video sub-events, accuracy of event recognition is further improved. First, the frame-level descriptors are extracted using convolutional neural networks (CNNs) pre-trained on ImageNet, which are fine-tuned on a portion of training video frames. Then, a spatio-temporal encoding is applied to the derived descriptors. The proposed spatio-temporal encoding, as the main contribution of this work, is inspired from the well-known vector of locally aggregated descriptors (VLAD) encoding in spatial domain and from total variation de-noising (TVD) in temporal domain. The proposed unified spatio-temporal encoding is then shown to be in the form of a convex optimization problem which is solved efficiently with alternating direction method of multipliers (ADMM) algorithm. The experimental results show superiority of the proposed encoding method in terms of recognition accuracy over both frame-level video encoding approaches and spatio-temporal video representations. As compared to the state-of-the-art approaches, our encoding method improves the mean average precision (mAP) over both Columbia consumer video (CCV), unstructured social activity attribute (USAA), YouTube-8M, and Kinetics datasets and is very competitive on FCVID dataset.
Employing Differentiable Neural Computers for Image Captioning and Neural Machine Translation

In the history of artificial neural networks, LSTMs have proved to be a high-performance architecture at sequential data learning. Although LSTMs are remarkable in learning sequential data but are limited in their ability to learn long-term dependencies and representation of certain data structures because of the lack of external memory. In this paper, we tackled two main tasks, one is language translation and other is image captioning. We approached the problem of language translation by leveraging the capabilities of the recently developed DNC architectures. Here we modified the DNC architecture by including dual neural controllers instead of one and an external memory module. Inside our controller, we employed a neural network with memory-augmentation which differs from the original differentiable neural computer, we implemented a dual controller’s system in which one controller is for encoding the query sequence whereas another controller is for decoding the translated sequences. During the encoding cycle, new inputs are read and the memory is updated accordingly. In the decoding cycle, the memory is protected from any writing from the decoding controller. Thus, the decoder phase generates a translated sequence at a time step. Therefore, the proposed dual controller neural network with memory-augmentation is then trained and tested on the Europarl dataset. For the image captioning task, our architecture is inspired by an end-to-end image captioning model where CNN’s output is passed to RNN as input only once and the RNN generates words depending on the input. We trained our DNC captioning model on 2015 MSCOCO dataset. In the end, we compared and shows the superiority of our architecture as compared to conventionally used LSTM and NTM architectures.
Provably constant-time planning and replanning for real-time grasping objects off a conveyor belt

In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick-and-place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. In addition to the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived from a distance. For fast-moving conveyor belts, the robot cannot wait for a perfect estimate before it starts executing its motion. In order to be able to reach the object in time, it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose a planning framework that meets these requirements by providing provable constant-time planning and replanning guarantees. To this end, we first introduce and formalize a new class of algorithms called constant-time motion planning (CTMP) algorithms that guarantee to plan in constant time and within a user-defined time bound. We then present our planning framework for grasping objects off a conveyor belt as an instance of the CTMP class of algorithms. We present it, provide its analytical properties, and perform an experimental analysis both in simulation and on a real robot.
3D Facial Similarity Measurement and Its Application in Facial Organization

We propose a novel framework for 3D facial similarity measurement and its application in facial organization. The construction of the framework is based on Kendall shape space theory. Kendall shape space is a quotient space that is constructed by shape features. In Kendall shape space, the shape features can be measured and is robust to similarity transformations. In our framework, a 3D face is represented by the facial feature landmarks model (FFLM), which can be regarded as the facial shape features. We utilize the geodesic in Kendall shape space to represent the FFLM similarity measurement, which can be regarded as the 3D facial similarity measurement. The FFLM similarity measurement is robust to facial expressions, head poses, and partial facial data. In our experiments, we compute the distance between different FFLMs in two public facial databases: FRGC2.0 and BosphorusDB. On average, we achieve a rank-one facial recognition rate of 98%. Based on the similarity results, we propose a method to construct the facial organization. The facial organization is a hierarchical structure that is achieved from the facial clustering by FFLM similarity measurement. Based on the facial organization, the performance of face searching in a large facial database can be improved obviously (about 400% improvement in experiments).
Quantum MDS and Synchronizable Codes From Cyclic and Negacyclic Codes of Length 2<i>p<sup>s</sup> </i> Over F<sub> <i>p<sup>m</sup> </i> </sub>

Let p be an odd prime, and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p(m)</sub> is the finite field of p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> elements. In this paper, all maximum distance separable (briefly, MDS) cyclic and negacyclic codes of length 2p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p(m)</sub> are established. As an application, all quantum MDS (briefly, qMDS) codes are constructed from cyclic and negacyclic codes of length 2p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over finite fields using the Calderbank Shor-Steane (briefly, CSS) and Hermitian constructions. These codes are new in the sense that their parameters are different from all the previous constructions. Furthermore, quantum synchronizable codes (briefly, QSCs) are obtained from cyclic codes of length 2p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p(m)</sub> . To enrich the variety of available QSCs, many new QSCs are constructed to illustrate our results. Among them, there are QSCs codes with shorter lengths and much larger minimum distances than known primitive narrow-sense Bose-Chaudhuri-Hocquenghem (briefly, BCH) codes.
Assembly support systems with Markov predictors

In this paper, we analyse Markov prediction as a suitable model to suggest the next assembly step in the manufacturing process. The goal is a decision support system which can assist the workers in a factory, at least in their training period, to manually assemble a product. We evaluate the proposed context-based predictor on a dataset collected through an experiment involving 68 trainees and compare it with our previously implemented two-level context-based predictors. The experiment consisted of assembling a tablet composed of seven components. We were interested in finding the most efficient prediction method that can be soundly employed in real-life settings.
Development of computer aided classroom teaching system based on machine learning prediction and artificial intelligence KNN algorithm

With the continuous development of science and technology, computer-aided teaching has become a common mode of school teaching. From the current situation, it can be seen that the current computer-aided teaching mostly replaces the traditional teaching mode with multimedia, and does not play the role of functional teaching, and teachers cannot effectively grasp the students’ psychological thoughts in teaching. Based on this, this study combines machine learning prediction and artificial intelligence KNN algorithm to actual teaching. Moreover, this study collects video and instructional images for student feature behavior recognition, and distinguishes individual features from group feature recognition, and can detect student expression recognition in detail. In addition, this study designed a case study to analyze the performance of the algorithm. From the experimental results, it can be seen that the proposed algorithm has certain effects and can be used as an algorithm to assist the teaching process and can provide theoretical reference for subsequent related research.