Reviewerly/exHarmony huggingface dataset:
Redesigning Curriculum in line with Industry 4.0

Development in information technology has resulted in revolutionizing all aspects of life and this revolution has been realized as Industrial Revolution 4.0 (IR4.0). The IR4.0 will reduce the gap between digital and physical world. Keeping ultramodern developments in view, an amalgamation of physical and digital systems will prove to be revolutionary. To elevate from the current state universities are required to prepare curriculum, academicians and students alike. Thus academia should weigh their capabilities putting effort to equip our generations with latest knowledge and skills to face future realism. Universities are required to enhance their approach and methods of education. Latest technologies such as Big Data Analytics, Artificial Intelligence, Augmented Reality, and Internet of Things, Cloud Computing and other advancements need to be focused in order for students to learn its application. This paper is based on qualitative research along with a deductive approach focused towards redesigning curriculum and teaching practices in line with IR4.0 practices. The research is focused upon various emerging challenges related to fundamental elements and objectives in academia. Based on the qualitative research a curriculum matrix has been proposed, the suggested matrix will enable universities to enhance their current curriculum in line with the technological competencies required in the upcoming era of Industry 4.0.
Feistel Structures for MPC, and More

Efficient PRP/PRFs are instrumental to the design of cryptographic protocols. We investigate the design of dedicated PRP/PRFs for three application areas - secure multiparty computation (MPC), ZKSNARK and zero-knowledge (ZK) based PQ signature schemes. In particular, we explore a family of PRFs which are generalizations of the well-known Feistel design approach followed in a previously proposed application specific design - MiMC. Attributing to this approach we call our family of PRP/PRFs GMiMC. In MPC applications, our construction shows improvements (over MiMC) in throughput by a factor of more than 4 and simultaneously a 5-fold reduction of preprocessing effort, albeit at the cost of a higher latency. Another use-case where MiMC outperforms other designs, in SNARK applications, our design GMiMCHash shows moderate improvement. Additionally, in this case our design benefits from the flexibility of using smaller (prime) fields. In the area of recently proposed ZK-based PQ signature schemes where MiMC was not competitive at all, our new design has 30 times smaller signature size than MiMC.
Unmanned vehicle path planning using a novel ant colony algorithm

The ant colony optimization algorithm is an effective way to solve the problem of unmanned vehicle path planning. First, establish the environment model of the unmanned vehicle path planning, process and describe the environmental information, and finally realize the division of the problem space. Next, the biomimetic behavior of the ant colony algorithm is described. The ant colony algorithm has been improved by adding a penalty strategy. This penalty strategy can enhance the utilization of resources and guide the ants to explore other unknown areas by using the worse value in the search history to enhance the volatility of the pheromone.
Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

We present Occlusion-Net, a framework to predict 2D and 3D locations of occluded keypoints for objects, in a largely self-supervised manner. We use an off-the-shelf detector as input (like MaskRCNN) that is trained only on visible key point annotations. This is the only supervision used in this work. A graph encoder network then explicitly classifies invisible edges and a graph decoder network corrects the occluded keypoint locations from the initial detector. Central to this work is a trifocal tensor loss that provides indirect self-supervision for occluded keypoint locations that are visible in other views of the object. The 2D keypoints are then passed into a 3D graph network that estimates the 3D shape and camera pose using the self-supervised re-projection loss. At test time, our approach successfully localizes keypoints in a single view under a diverse set of severe occlusion settings. We demonstrate and evaluate our approach on synthetic CAD data as well as a large image set capturing vehicles at many busy city intersections. As an interesting aside, we compare the accuracy of human labels of invisible keypoints against those obtained from geometric trifocal-tensor loss.
Hierarchical Disentanglement of Discriminative Latent Features for Zero-Shot Learning

Most studies in zero-shot learning model the relationship, in the form of a classifier or mapping, between features from images of seen classes and their attributes. Therefore, the degree of a model's generalization ability for recognizing unseen images is highly constrained by that of image features and attributes. In this paper, we discuss two questions about generalization that are seldom discussed. Are image features trained with samples of seen classes expressive enough to capture the discriminative information for both seen and unseen classes? Is the relationship learned from seen image features and attributes sufficiently generalized to recognize unseen classes. To answer these two questions, we propose a model to learn discriminative and generalizable representations from image features under an auto-encoder framework. The discriminative latent features are learned through a group-wise disentanglement over feature groups with a hierarchical structure. On popular benchmark data sets, a significant improvement over state-of-the-art methods in tasks of typical and generalized zero-shot learning verifies the generalization ability of latent features for recognizing unseen images.
Learning Binary Code for Personalized Fashion Recommendation

With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great needs. Recommending fashion outfits, each of which is composed of multiple interacted clothing and accessories, is relatively new to the field. The problem becomes even more interesting and challenging when considering users' personalized fashion style. Another challenge in a large-scale fashion outfit recommendation system is the efficiency issue of item/outfit search and storage. In this paper, we propose to learn binary code for efficient personalized fashion outfits recommendation. Our system consists of three components, a feature network for content extraction, a set of type-dependent hashing modules to learn binary codes, and a matching block that conducts pairwise matching. The whole framework is trained in an end-to-end manner. We collect outfit data together with user label information from a fashion-focused social website for the personalized recommendation task. Extensive experiments on our datasets show that the proposed framework outperforms the state-of-the-art methods significantly even with a simple backbone.
Music Recommendation System Using Human Activity Recognition From Accelerometer Data

Music listening is a very personal and situational behavior for modern people who always carry smartphones in everyday life. Therefore, contextual information, such as user's current activity and mood state could be used to greatly improve music recommendations. In this paper, we develop a smartphone-based mobile system that includes two core modules for recognizing human activities and then accordingly recommending music. In the proposed method, a deep residual bidirectional gated recurrent neural network is applied to obtain high activity recognition accuracy from accelerometer signals on the smartphone. In order to improve the performance of tempo-oriented music classification, an ensemble of dynamic classification using a long-term modulation spectrum and sequence classification using a short-term spectrogram is used. Music recommendation is performed using the relationship between the recognized human activities and the music files indexed by tempo-oriented music classification that reflects user preference models in order to achieve high user satisfaction. The results of comprehensive experiments on real data confirm the accuracy of the proposed activity-aware music recommendation framework.
ListeningCups

In this pictorial, we present the ListeningCups: a set of 3D printed porcelain cups embedded with datasets of everyday ambient sounds. During a one-week pilot project, a ceramic artist and an interaction design researcher collaborated to explore meaning making around everyday data (sound in our case). We developed a workflow to capture data, prepare datasets, transcribe data from decibels to G-code, and create a set of 3D printed porcelain cups which represent this data in a textural and tactile form. We discuss how our work also included aesthetic investigative practices as well as data accidents. We conclude by contributing two concepts-data tactility and data stories-that can serve as starting points for designers, artists, or researchers interested in the intersection of materiality, data, fabrication, and ceramics.
Bandwidth prediction in low-latency chunked streaming

HTTP adaptive streaming with chunked transfer encoding can be used to offer low-latency streaming without sacrificing the coding efficiency. While this allows a media segment to be generated and delivered at the same time, which is critical in reducing the latency, the conventional bitrate adaptation schemes make often grossly inaccurate bandwidth measurements due to the presence of idle periods between the chunks. These wrong measurements cause the streaming client to make bad adaptation decisions. To this end, we design ACTE, a new bitrate adaptation scheme that leverages the unique nature of chunk downloads. ACTE uses a sliding window to accurately measure the available bandwidth and an online linear adaptive filter to predict the bandwidth into the future. Results show that ACTE achieves 96% measurement accuracy, which translates to a 65% reduction in the number of stalls and a 49% increase in quality of experience on average compared to other schemes.
Exploration of Complementary Features for Speech Emotion Recognition Based on Kernel Extreme Learning Machine

Previous studies of speech emotion recognition using either empirical features (e.g., F0, energy, and voice probability) or spectrogram-based statistical features. The empirical features can highlight the human knowledge of emotion recognition, while the statistical features enable a general representation, but they do not emphasize human knowledge sufficiently. However, the use of these two kinds of features together can complement some features that may be unconsciously used by humans in daily life but have not been realized yet. Based on this consideration, this paper proposes a dynamic fusion framework to utilize the potential advantages of the complementary spectrogram-based statistical features and the auditory-based empirical features. In addition, a kernel extreme learning machine (KELM) is adopted as the classifier to distinguish emotions. To validate the proposed framework, we conduct experiments on two public emotional databases, including Emo-DB and IEMOCAP databases. The experimental results demonstrate that the proposed fusion framework significantly outperforms the existing state-of-the-art methods. The results also show that the proposed method, by integrating the auditory-based features with spectrogram-based features, could achieve a notably improved performance over the conventional methods.
Quantum thermometry by single-qubit dephasing

We address the dephasing dynamics of a qubit as an effective process to estimate the temperature of its environment. Our scheme is inherently quantum, since it exploits the sensitivity of the qubit to decoherence, and does not require thermalization with the system under investigation. We optimize the quantum Fisher information with respect to the interaction time and the temperature in the case of Ohmic-like environments. We also find explicitly the qubit measurement achieving the quantum Cramér-Rao bound to precision. Our results show that the conditions for optimal estimation originate from a non-trivial interplay between the dephasing dynamics and the Ohmic structure of the environment. In general, optimal estimation is achieved neither when the qubit approaches the stationary state, nor for full dephasing.
Anomaly Detection in Real-Time Multi-Threaded Processes Using Hardware Performance Counters

We propose a novel methodology for real-time monitoring of software running on embedded processors in cyber-physical systems (CPS). The approach uses real-time monitoring of hardware performance counters (HPC) and applies to multi-threaded and interrupt-driven processes typical in programmable logic controller (PLC) implementation of real-time controllers. The methodology uses a black-box approach to profile the target process using HPCs. The time series of HPC measurements over a time window under known-good operating conditions is used to train a machine learning classifier. At run-time, this trained classifier classifies the time series of HPC measurements as baseline (i.e., probabilistically corresponding to a model learned from the training data) or anomalous. The baseline versus anomalous labels over successive time windows offer robustness against the stochastic variability of code execution on the embedded processor and detect code modifications. We demonstrate effectiveness of the approach on an embedded PLC in a hardware-in-the-loop (HITL) testbed emulating a benchmark industrial process. In addition, to illustrate the scalability of the approach, we also apply the methodology to a second PLC platform running a representative embedded control process.
Lead-Lag relationship between Bitcoin and Ethereum: Evidence from hourly and daily data

This paper investigates lead-lag relationship between heavyweight cryptocurrencies Bitcoin and Ethereum. Traditional studies of information flow between markets preponderate on cash vs. futures, whereby researchers are interested in the stabilizing impact of futures on spot markets. While interest in the same relationship in the nascent cryptocurrency sphere is emerging, little is known regarding price leadership between these assets. In this paper, we employ a battery of statistical tests—VECM, Granger Causality, ARMA, ARDL and Wavelet Coherence—to identify price leadership between the two crypto heavyweights Bitcoin and Ethereum. Based on one year hourly and daily data from August 2017 through to September 2018, our tests yield varied results but largely suggest bi-directional causality between the two assets. Moreover, the results indicate that intraday crypto traders can barely exploit Bitcoin-Ethereum hourly or daily price discovery process to their advantage.
A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision making (MCDM)-based approach to evaluate imbalanced classifiers in credit and bankruptcy risk prediction by considering multiple performance metrics simultaneously. An experimental study is designed to provide a comprehensive evaluation of imbalanced classifiers using the proposed evaluation approach over seven financial imbalanced data sets from the UCI Machine Learning Repository. The TOPSIS, a well-known MCDM method, was applied to rank three categories of imbalanced classifiers using six popular evaluation criteria. The rankings results indicate that: 1) the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion; and 2) Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of imbalanced learning approaches. Specifically, SMOTEBoost-C4.5, SMOTE-C4.5, and SMOTE-MLP were ranked as the top three classifiers based on their performances on the six criteria.
Prediction of solar irradiation and performance evaluation of grid connected solar 80KWp PV plant in Bangladesh

Energy security is the triggering factor for a developing country. Thus, for ensuring energy security renewable energy such as PV plant could be a best alternative. This study deals with the performance analysis of 80 KWp grid-connected solar power plant in Dhaka. This study presents a solar irradiation predict model based on fuzzy logic and artificial neural networks which aims to achieve a good accuracy at different weather conditions. The accuracy of predicted solar irradiation will affect the power output forecast of grid-connected photovoltaic systems which is important for power system operation and planning. Used data are taken from NASA. The performance of ANN is better than fuzzy logic model by comparing RMSE, R 2 and percentage of accuracy. These fuzzy logic and ANN models can be used to forecast solar irradiation of Dhaka city in different environment condition and to provide enough information about the feasibility of solar power projects.
Few-shot learning-based human activity recognition

Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, a few-shot learning method for wearable sensor based human activity recognition, which is a technique that seeks high-level human activity knowledge from low-level sensor inputs, is proposed. Due to the high costs to obtain human generated activity data and the ubiquitous similarities between activity modes, it can be more efficient to borrow information from existing activity recognition models than to collect more data to train a new model from scratch when only a few data are available for model training. The proposed few-shot human activity recognition method leverages a deep learning model for feature extraction and classification while knowledge transfer is performed in the manner of model parameter transfer. In order to alleviate negative transfer, a metric is proposed to measure cross-domain class-wise relevance so that knowledge of higher relevance is assigned larger weights during knowledge transfer. Promising results in extensive experiments show the advantages of the proposed approach.
On Both Cold-Start and Long-Tail Recommendation with Social Data

The number of “hits” has been widely regarded as the lifeblood of many web systems, e.g., e-commerce systems, advertising systems and multimedia consumption systems. However, users would not hit an item if they cannot see it, or they are not interested in the item. Recommender system plays a critical role of discovering interesting items from near-infinite inventory and exhibiting them to potential users. Yet, two issues are crippling the recommender systems. One is “how to handle new users”, and the other is “how to surprise users”. The former is well-known as cold-start recommendation. In this paper, we show that the latter can be investigated as long-tail recommendation. We also exploit the benefits of jointly challenging both cold-start and long-tail recommendation, and propose a novel approach which can simultaneously handle both of them in a unified objective. For the cold-start problem, we learn from side information, e.g., user attributes, user social relationships, etc. Then, we transfer the learned knowledge to new users. For the long-tail recommendation, we decompose the overall interesting items into two parts: a low-rank part for short-head items and a sparse part for long-tail items. The two parts are independently revealed in the training stage, and transfered into the final recommendation for new users. Furthermore, we effectively formulate the two problems into a unified objective and present an iterative optimization algorithm. A fast extension of the method is proposed to reduce the complexity, and extensive theoretical analysis are provided to proof the bounds of our approach. At last, experiments of social recommendation on various real-world datasets, e.g., images, blogs, videos and musics, verify the superiority of our approach compared with the state-of-the-art work.
Evolutionary Collaborative Human-UAV Search for Escaped Criminals

The use of unmanned aerial vehicles (UAVs) for target searching in complex environments has increased considerably in recent years. The numerous studies on UAV search methods have been reported, but few have been conducted on collaborative human-UAV search which is common in many applications. In this paper, we present a problem of collaborative human-UAV search for escaped criminals, the aim of which is to minimize the expected time of capture rather than detection. We show that our problem is much more complex than the problem of pure UAV search. The difficulty of our problem is further increased by the fact that criminals will attempt to avoid detection and capture. To solve the problem, we propose a hybrid evolutionary algorithm (EA) that uses three evolutionary operators, namely, comprehensive learning, variable mutation, and local search, to efficiently explore the solution space. The experimental results demonstrate that the proposed method outperforms some well-known EAs and other popular UAV search methods on test instances. An application of our method to a real-world operation took 311 min to capture a criminal who had escaped for over three days, validating its practicability and performance advantage. This paper provides a good basis for promoting the application of EAs to a wider class of man-machine collaboration scheduling problems.
An End-to-End Attack on Text CAPTCHAs

Text-based CAPTCHAs are the most widely used CAPTCHA scheme. Most text-based CAPTCHAs have been cracked. However, previous works have mostly relied on a series of preprocessing steps to attack text CAPTCHAs, which was complicated and inefficient. In this paper, we introduce a simple, generic, and effective end-to-end attack on text CAPTCHAs without any preprocessing. Through a convolutional neural network and an attention-based recurrent neural network, our attack broke a wide range of real-world text CAPTCHAs that are deployed by the top 50 most popular websites ranked by Alexa.com. In addition, this paper comprehensively analyzed the security of most resistance mechanisms of text-based CAPTCHAs through experiments. Experimental results prove that the anti-segmentation principle can be completely broken under deep learning attacks without any segmentation or preprocessing steps in contrast to commonly held beliefs.