Reviewerly/exHarmony huggingface dataset:
Dilated temporal relational adversarial network for generic video summarization

The large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. We propose a novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to achieve frame-level video summarization. Given a video, it selects the set of key frames, which contain the most meaningful and compact information. Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner. A new dilated temporal relation (DTR) unit is introduced to enhance temporal representation capturing. The generator uses this unit to effectively exploit global multi-scale temporal context to select key frames and to complement the commonly used Bi-LSTM. To ensure that summaries capture enough key video representation from a global perspective rather than a trivial randomly shorten sequence, we present a discriminator that learns to enforce both the information completeness and compactness of summaries via a three-player loss. The loss includes the generated summary loss, the random summary loss, and the real summary (ground-truth) loss, which play important roles for better regularizing the learned model to obtain useful summaries. Comprehensive experiments on three public datasets show the effectiveness of the proposed approach.
Review and Visualization of Facebook's FastText Pretrained Word Vector Model

One of the most popular machine learning methods for processing natural language is Word2Vec. Like several other machine learning methods, there are some concerns regarding the interpretability of the resulting model. In this paper, our research aims to review and analyze a pretrained word vector model for processing Bahasa Indonesia released by Facebook, FastText. The analysis process is started by comparing words existing in the pretrained model and in the official dictionary of Indonesian language (KBBI), then words in the model are visualized to provide further analysis and review. A combination of Principal Component Analysis (PCA) method and t-SNE algorithm is used as a dimensionality reduction technique to visualize the word set. Based on the analysis and visualization result, this paper proposes several considerations needed when using the FastText pretrained word vector model to process natural language in Indonesian such as whether or not common natural language text preprocessing techniques are needed.
Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images

Segmentation maps of medical images annotated by medical experts contain rich spatial information. In this paper, we propose to decompose annotation maps to learn disentangled and richer feature transforms for segmentation problems in medical images. Our new scheme consists of two main stages: decompose and integrate. Decompose: by annotation map decomposition, the original segmentation problem is decomposed into multiple segmentation sub-problems; these new segmentation sub-problems are modeled by training multiple deep learning modules, each with its own set of feature transforms. Integrate: a procedure summarizes the solutions of the modules in the previous stage; a final solution is then formed for the original segmentation problem. Multiple ways of annotation map decomposition are presented and a new end-to-end trainable K-to-1 deep network framework is developed for implementing our proposed "decompose-and-integrate" learning scheme. In experiments, we demonstrate that our decompose-and-integrate segmentation scheme, utilizing state-of-the-art fully convolutional networks (e.g., DenseVoxNet in 3D and CUMedNet in 2D), improves segmentation performance on multiple 3D and 2D datasets. Ablation study confirms the effectiveness of our proposed learning scheme for medical images.
Hybrid Activity Recognition for Ballroom Dance Exercise using Video and Wearable Sensor

In this paper, we propose a hybrid activity recognition method for ballroom dance exercise using video and wearable sensor. The purpose of our research is to design a mechanism to support ballroom dance exercise, and this paper reports the first part to design a mechanism is to support ballroom dance exercise, and this paper reports the first outcome to achieve the purpose - recognizing ballroom dance exercise. There are two conceivable ways to recognize dance exercise: videos and wearable sensor. However, each of them has its disadvantages. Using video is a good way to recognize the movement of the body. However, it cannot provide us accurate timing or strength of foot actions because the number of their flames per seconds is too small to recognize the fast movements of dancers. On the other hand, while a wearable sensor is good at recognizing foot timing and strength, it is not good at recognizing the movement of the whole body. Therefore we propose a hybrid recognition method utilizing the merits of both video and wearable sensor. This paper focuses to recognize four different types of steps in Latin American, a kind of ballroom dance. For each step, we record wearable sensing data and videos. As a result, it is found that the accuracy of step recognition is improved by adding wearable sensing data to video data shot from two different angles.
Comparison of Image processing techniques for detecting human presence in an image

The performance analysis of Haar-like features and Histogram of Oriented Gradients suited for detecting human presence in an image is presented here. The algorithms are implemented using OpenCV on an embedded platform. The algorithms were evaluated for a dataset comprising of 2850 images. The implementation details, comparison of algorithms and results obtained are discussed in detail.
Analyzing center/surround retinex

The receptive fields in the Human Visual System (HVS) are organized in such a way that they not merely capture information on the photoreceptor's exposition to light but also on the differences in firing rates of their center and surround cells. This organization can be related with the human's ability to cope with the high dynamic range of natural scenes. As a way to mimic this behavior E. Land proposed in 1986 a perception model consisting in two steps, namely, a local adaptation followed by a global transform. This model gave rise to the so-called Center/Surround tone-mapping algorithms, which are used to map the intensity values of high dynamic range (HDR) images to the limited range of displayable images (typically using 8-bits per channel). In this paper we unify the different Center/Surround algorithms proposed in the literature using a common framework and analyze several possibilities for the local and global operations involved. We accompany our study with quantitative and qualitative results that permit us to suggest the best pair of local/global transforms for a Center/Surround method.
Improved Cryptanalysis on SipHash

SipHash is an ARX-based pseudorandom function designed by Aumasson and Bernstein for short message inputs. Recently, Ashur et al. proposed an efficient analysis method against ARX algorithm—"Rotational-XOR cryptanalysis". Inspired by their work, we mount differential and Rotational-XOR cryptanalysis on two instances of SipHash-1-x and SipHash-2-x in this paper, where SipHash-1-x (or SipHash-2-x) represents the Siphash instance with one (or two) compression round and x finalization rounds. Firstly, we construct the search model for colliding characteristic and RX-colliding characteristic on SipHash. Based on the model, we find the colliding characteristics and RX-colliding characteristics of SipHash by the SMT-based automatic search tool. Moreover, we give a formula for the selection of initial constants to improve the resistance of Siphash against Rotational-XOR cryptanalysis to make the algorithm safer. In addition, we find an RX-colliding characteristic with probability $$2^{-93.6}$$ for a revised version of SipHash-1-x with one message block, and an RX-colliding characteristic with probability $$2^{-160}$$ for a revised version of SipHash-1-x with two message blocks. With the SMT-based technique, which outputs one message pair of the RX-collision if the given characteristic has a nonzero probability. Finally, with the RX-colliding characteristic we found earlier, we give the RX-collision with message pair and key of a revised version of SipHash-1-x with one message block.
Guessing Individual Sequences: Generating Randomized Guesses Using Finite–State Machines

Motivated by earlier results on universal randomized guessing, we consider an individual-sequence approach to the guessing problem: in this setting, the goal is to guess a secret, individual (deterministic) vector x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> = (x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . .. , x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ), by using a finite-state machine that sequentially generates randomized guesses from a stream of purely random bits. We define the finite-state guessing exponent as the asymptotic normalized logarithm of the minimum achievable moment of the number of randomized guesses, generated by any finite-state machine, until xn is guessed successfully. We show that the finite-state guessing exponent of any sequence is intimately related to its finite-state compressibility (due to Lempel and Ziv), and it is asymptotically achieved by the decoder of (a certain modified version of) the 1978 Lempel-Ziv data compression algorithm (a.k.a. the LZ78 algorithm), fed by purely random bits. The results are also extended to the case where the guessing machine has access to a side information sequence, y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> = (y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . .. , y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ), which is also an individual sequence.
Gaze-based Human Factors Measurements for the Evaluation of Intuitive Human-Robot Collaboration in Real-time

Human attention processes play a key role in optimizations of human-robot collaboration (HRC) systems. We describe a novel framework to assess the human state primarily by gaze and in real-time by deriving parameters about situation awareness which is fundamental in the evaluation of collaboration. Comprehensive experiments on HRC were conducted with typical tasks including collaborative pick-and-place in a lab based prototypical manufacturing environment. The methodology measures executive functions and situation awareness in the HRC task in real-time for the purpose of human factors-based performance evaluation in HRC applications.
An efficient co-Attention Neural Network for Social Recommendation

The recent boom in social networking services has prompted the research of recommendation systems. The basic assumption behind these works was that "users' preference is similar to or influenced by their friends". Although many studies have attempted to use social relations to enhance recommendation system, they neglected that the heterogeneous nature of online social networks and the variations in users' trust of friends according to different items. As a natural symmetry between the latent preference vector of the user and her friends, we propose a new social recommendation method called ScAN (short for "co-Attention Neural Network for Social Recommendation"). ScAN is based on a co-attention neural network, which learns the influence value between the user and her friends from the historical data of the interaction between the user/her friends and an item. When the user interacts with different items, different attention weights are assigned to the user and her friends respectively, and the user's new latent preference feature is obtained through an aggregation strategy. To enhance the recommendation performance, a network embedding technique is utilized as a pre-training strategy to extract the users' embedding and to incorporate the extracted factors into the neural network model. By conducting extensive experiments on three different real-world datasets, we demonstrate that our proposed method ScAN achieves a superior performance for all datasets compare with state-of-the-art baseline methods in social recommendation task.
SMOTEFRIS-INFFC: Handling the challenge of borderline and noisy examples in imbalanced learning for software defect prediction

The object of Software Defect Prediction (SDP) is to identify modules that are prone to defect. This is achieved by training prediction models with datasets obtained by mining software historical depositories. When one acquires data through this approach, it often includes class imbalance which has an unequal class representation among their example. We hypothesize that the imbalance learning is not a problem in itself and decrease in performance is also influenced by other factors related to class distribution in the data. One of these is the existence of noisy and borderline examples. Thus, the objective of our research is to propose a novel preprocessing method using Synthetic Minority Over-Sampling Technique (SMOTE), Fuzzy-rough Instance Selection type II (FRIS-II) and Iterative Noise Filter based on the Fusion of Classifiers (INFFC) which can overcome these problems. The experimental results show that the new proposal significantly outperformed all the methods compared in this study.
Privacy Preserved Decentralized Deep Learning: A Blockchain Based Solution for Secure AI-Driven Enterprise

Deep learning and Blockchain attracted the attention of both the research community and the industry. In the financial enterprise by using the Blockchain technology, financial transactions could be performed in shorter periods and with higher transparency and security. In Blockchain ecosystem, there is no need for having a central reliable authority to regulate and control the system. In Blockchain many entities which cannot trust each other in normal conditions can join together to achieve a mutual goal. Deep learning algorithms are currently the best solution for many machine learning applications and provide high accuracy models for robotics, computer vision, smart cities and other AI-driven enterprise. However, availability of more data can boost the performance of deep models considerably. In this paper, a secure decentralized deep learning framework for big data analytics on Blockchain for AI-driven enterprise is proposed. The proposed framework uses the Stellar Blockchain infrastructure for secure decentralized training of the deep models. A Deep Learning Coin (DLC) is used for Blockchain compensation. The security of the proposed framework incentivizes people and organizations to share their valuable data for training the deep neural models while the privacy of their data is preserved.
End‐to‐end double JPEG detection with a 3D convolutional network in the DCT domain

Detection of double JPEG compression is essential in the field of digital image forensics. Although double JPEG compression detection methods have greatly improved with the development of convolutional neural networks (CNNs), they rely on handcrafted features such as discrete cosine transform (DCT) histograms. In this Letter, the authors propose an end-to-end trainable 3D CNN in the DCT domain for double JPEG compression detection. Moreover, they also propose a new type of module, called feature rescaling, to insert the quantisation table into the network suitably. The experiments show that the proposed method outperforms state-of-the-art methods.
Culture<i>by</i>babies: Imagining everyday material culture through babies’ engagements with socks

This article takes its point of departure in babies’ engagements with socks and seeks to explore (1) how material culture matters in babies’ everyday lives and (2) how we can understand material culture through attending to babies’ own practices, that is, babies’ culture. The ongoingness, sensoriality and movement of material culture are highlighted, and the article concludes that re-thinking material culture through babies’ engagements with socks means shifting the focus away from objects’ established meaning and towards the materials of those objects.
PlasticCoin: an ERC20 Implementation on Hyperledger Fabric for Circular Economy and Plastic Reuse

Cryptocurrencies have gained popularity in the last few years, thanks to the democratization of Bitcoin. However, most of these currencies have a very general purpose, namely of allowing people to pay their products with an electronic, decentralised currency. Moreover, the lack of trust of these open-network blockchains comes at a significant economic cost. In this paper, we present PlasticCoin, a cryptocurrency empowering plastic reuse in a circular economical model, that anyone can join, and that respects the ERC20 specifications on a consortium blockchain. We also explore the economical ecosystem revolving around PlasticCoin, and also introduce a way to print tickets that can temporarily hold the role of physical banknotes. Finally, we show that our system is flexible, and it can be adapted to many other business purposes.
Assessing the Threat of Blockchain-based Botnets

Time and time again the security community has faced novel threats that were previously never analyzed, sometimes with catastrophic results. To avoid this, proactive analysis of envisioned threats is of great importance. One such threat is blockchain-based botnets. Bitcoin, and blockchain-based decentralized cryptocurrencies in general, promise a fair and more transparent financial system. They do so by implementing an open and censorship-resistant atomic broadcast protocol that enables the maintenance of a global transaction ledger, known as a blockchain. In this paper, we consider how this broadcast protocol may be used for malicious behavior as a botnet command and control (C2) channel. Botmasters have been known to misuse broadcasting platforms, like social media, as C2 channels. However, these platforms lack the integral censorship-resistant property of decentralized cryptocurrencies. In this paper, we provide a comprehensive systematization of knowledge study on using blockchains as botnet C2 channels, generating a number of important insights. We set off by providing a critical analysis of the state of the art of blockchain-based botnets, along with an abstract model of such a system. We then examine the inherent limitations of the design, in an attempt to challenge the feasibility of such a botnet. With such limitations in mind, we move forward with an experimental analysis of the detectability of such botnets and discuss potential countermeasures. Contrary to previous work that proposed such botnets, we provide a broad overview of the associated risk and view the problem in relation to other existing botnet C2 channels. We conclude that despite its limitations, the blockchain, as a backup mechanism, practically renders attempts to suppress the control channel of a botnet futile. Thus, more focus should be put on detecting and disinfecting machines at the network edge (router) or even per-bot level.
Pursuit Sensing: Extending Hand Tracking Space in Mobile VR Applications

Field of view limitations are one of the major persisting setbacks for camera-based motion tracking systems and the need for flexible ways to improve capture volumes remains. We present Pursuit Sensing, a technique to considerably extend the tracking volume of a camera sensor through self-actuated reorientation using a customized gimbal, thus enabling a Leap Motion to dynamically follow the user's hand position in mobile HMD scenarios. This technique provides accessibility and high hardware compatibility for both users and developers while remaining simple and inexpensive to implement. Our technical evaluation shows that the proposed solution successfully increases hand tracking volume by 142% in pitch and 44% in yaw compared to the camera's base FOV, while featuring low latency and robustness against fast hand movements.
TNFS resistant families of pairing-friendly elliptic curves

Recently there has been a significant progress on the tower number field sieve (TNFS) method, reducing the complexity of the discrete logarithm problem (DLP) in finite field extensions of composite degree. These new variants of the TNFS attacks have a major impact on pairing-based cryptography and particularly on the selection of the underlying elliptic curve groups and extension fields. In this paper we revise the criteria for selecting pairing-friendly elliptic curves considering these new TNFS attacks in finite extensions of composite embedding degree. Additionally we update the criteria for finite extensions of prime degree in order to meet today's security requirements.
Towards a Holistic Definition of Requirements Debt

When not appropriately managed, technical debt is considered to have negative effects to the long term success of software projects. However, how the debt metaphor applies to requirements engineering in general, and to requirements engineering activities in particular, is not well understood. Grounded in the existing literature, we present a holistic definition of requirements debt which includes debt incurred during the identification, formalization, and implementation of requirements. We outline future assessment to validate and further refine our proposed definition. This conceptualization is a first step towards a requirements debt monitoring framework to support stakeholders decisions, such as when to incur and eventually pay back requirements debt, and at what costs.
A complex process fault diagnosis method based on manifold distribution adaptation

The main challenge for complex industrial processes is that the distribution of new process data is different from that of old process data, resulting in poor performance when monitoring the new process (target domain) using the model established by the old process (source domain) data. Moreover, new process data is often unlabeled, which is more common in actual industrial processes. Therefore, this paper proposes a novel fault diagnosis method for complex industrial processes based on Manifold Distribution Adaptation (MDA) to cope with this challenge. Specifically, in order to avoid feature degradation caused by feature transformation directly in the original feature space, MDA first maps source domain data and target domain data to Grassmann manifold through geodesic flow kernel. Second, dynamic distribution adaptation and source domain instance re-weighting are performed simultaneously on the Grassmann manifold to reduce the shift between the domains. Then, the base classifier trained by the adaptive source domain data can achieve accurate classification of the target domain data. Finally, the superiority of MDA is verified on the public transfer learning datasets and the actual industrial process datasets.