Reviewerly/exHarmony huggingface dataset:
BitMEX bitcoin derivatives: Price discovery, informational efficiency, and hedging effectiveness

Abstract BitMEX is the largest unregulated bitcoin derivatives exchange, listing contracts suitable for leverage trading and hedging. Using minute‐by‐minute data, we examine its price discovery and hedging effectiveness. We find that BitMEX derivatives lead prices on major bitcoin spot exchanges. Bid–ask spreads, interexchange spreads, and relative trading volumes are important determinants of price discovery. Further analysis shows that BitMEX derivatives have positive net spillover effects, are informationally more efficient than bitcoin spot prices, and serve as effective hedges against spot price volatility. Our evidence suggests that regulators prioritize the investigation of the legitimacy of BitMEX and its contracts.
Improving defect prediction with deep forest

Software defect prediction is important to ensure the quality of software. Nowadays, many supervised learning techniques have been applied to identify defective instances (e.g., methods, classes, and modules). However, the performance of these supervised learning techniques are still far from satisfactory, and it will be important to design more advanced techniques to improve the performance of defect prediction models. We propose a new deep forest model to build the defect prediction model (DPDF). This model can identify more important defect features by using a new cascade strategy, which transforms random forest classifiers into a layer-by-layer structure. This design takes full advantage of ensemble learning and deep learning. We evaluate our approach on 25 open source projects from four public datasets (i.e., NASA, PROMISE, AEEEM and Relink). Experimental results show that our approach increases AUC value by 5% compared with the best traditional machine learning algorithms. The deep strategy in DPDF is effective for software defect prediction.
Finite Speed of Quantum Scrambling with Long Range Interactions

In a locally interacting many-body system, two isolated qubits, separated by a large distance $r$, become correlated and entangled with each other at a time $t \ge r/v$. This finite speed $v$ of quantum information scrambling limits quantum information processing, thermalization and even equilibrium correlations. Yet most experimental systems contain long range power law interactions -- qubits separated by $r$ have potential energy $V(r)\propto r^{-\alpha}$. Examples include the long range Coulomb interactions in plasma ($\alpha=1$) and dipolar interactions between spins ($\alpha=3$). In one spatial dimension, we prove that the speed of quantum scrambling remains finite for sufficiently large $\alpha$. This result parametrically improves previous bounds, compares favorably with recent numerical simulations, and can be realized in quantum simulators with dipolar interactions. Our new mathematical methods lead to improved algorithms for classically simulating quantum systems, and improve bounds on environmental decoherence in experimental quantum information processors.
Toyota Smarthome: Real-World Activities of Daily Living

The performance of deep neural networks is strongly influenced by the quantity and quality of annotated data. Most of the large activity recognition datasets consist of data sourced from the web, which does not reflect challenges that exist in activities of daily living. In this paper, we introduce a large real-world video dataset for activities of daily living: Toyota Smarthome. The dataset consists of 16K RGB+D clips of 31 activity classes, performed by seniors in a smarthome. Unlike previous datasets, videos were fully unscripted. As a result, the dataset poses several challenges: high intra-class variation, high class imbalance, simple and composite activities, and activities with similar motion and variable duration. Activities were annotated with both coarse and fine-grained labels. These characteristics differentiate Toyota Smarthome from other datasets for activity recognition. As recent activity recognition approaches fail to address the challenges posed by Toyota Smarthome, we present a novel activity recognition method with attention mechanism. We propose a pose driven spatio-temporal attention mechanism through 3D ConvNets. We show that our novel method outperforms state-of-the-art methods on benchmark datasets, as well as on the Toyota Smarthome dataset. We release the dataset for research use.
Curriculum Learning for Natural Language Understanding

With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks.At the fine-tune stage, target task data is usually introduced in a completely random order and treated equally.However, examples in NLU tasks can vary greatly in difficulty, and similar to human learning procedure, language models can benefit from an easy-to-difficult curriculum.Based on this idea, we propose our Curriculum Learning approach.By reviewing the trainset in a crossed way, we are able to distinguish easy examples from difficult ones, and arrange a curriculum for language models.Without any manual model architecture design or use of external data, our Curriculum Learning approach obtains significant and universal performance improvements on a wide range of NLU tasks.
AdaBins: Depth Estimation Using Adaptive Bins

We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.
A systematic review and research perspective on recommender systems

Abstract Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system.
MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection

Moving object detection (MOD) in videos is a challenging task. Estimation of accurate background is the key to extracting the foreground from video frames. In this paper, we have proposed a novel compact end-to-end convolutional neural network architecture, motion saliency foreground network (MSFgNet), to estimate the background and to extract the foreground from video frames. Initially, the long streaming video is divided into a number of small video streams (SVS). The proposed network takes the SVS as an input and estimates the background frame for each SVS. Second, the saliency map is extracted using the current video frame and estimated background. Furthermore, a compact encoder-decoder network is proposed to extract the foreground from the estimated saliency maps. The performance of the proposed MSFgNet is tested on three benchmark datasets (CDnet-2014, LASIESTA, and PTIS) for MOD. The computational complexity (handling of number of parameters and execution time) and the performance of the proposed MSFgNet are compared with the existing state-of-the-art methods for MOD in terms of precision, recall, and F-measure. Performance analysis shows that the proposed network is very compact and outperforms the existing state-of-the-art methods for MOD in videos.
Enhancing underwater optical imaging by using a low-pass polarization filter

Object identification in highly turbid optical media depends mainly on the quality of collected images.Underwater images acquired in a turbid environment are generally of very poor quality.Attenuation and backscattering of light by water, by materials dissolved in the water, and by particulate material are the main causes of the degradation of underwater images.It is therefore essential to improve the quality of such images to facilitate object identification.The focus of this paper is to report the principle and validation of a fast and effective method of improving the quality of underwater images.On the one hand, this method uses a polarimetric imaging optical system to reduce the effect of diffusion on the image acquisition.On the other hand, it is based on an optimized version of the dark channel prior (DCP) method that has received a great deal of attention for image dehazing.Results derived from images obtained in a controlled laboratory water tank environment with different turbidity conditions and images from tests using the proposed method at sea demonstrate an ability to significantly improve visibility and reduce runtime by a factor of about 50 for a 4K image when compared to conventional DCP methods.
Deep Learning-based Job Placement in Distributed Machine Learning Clusters

Production machine learning (ML) clusters commonly host a variety of distributed ML workloads, e.g., speech recognition, machine translation. While server sharing among jobs improves resource utilization, interference among co-located ML jobs can lead to significant performance downgrade. Existing cluster schedulers (e.g., Mesos) are interference-oblivious in their job placement, causing suboptimal resource efficiency. Interference-aware job placement has been studied in the literature, but was treated using detailed workload profiling and interference modeling, which is not a general solution. This paper presents Harmony, a deep learning-driven ML cluster scheduler that places training jobs in a manner that minimizes interference and maximizes performance (i.e., training completion time). Harmony is based on a carefully designed deep reinforcement learning (DRL) framework augmented with reward modeling. The DRL employs state-of-the-art techniques to stabilize training and improve convergence, including actor-critic algorithm, job-aware action space exploration and experience replay. In view of a common lack of reward samples corresponding to different placement decisions, we build an auxiliary reward prediction model, which is trained using historical samples and used for producing reward for unseen placement. Experiments using real ML workloads in a Kubernetes cluster of 6 GPU servers show that Harmony outperforms representative schedulers by 25% in terms of average job completion time.
Optical Interferometry with Quantum Networks

We propose a method for optical interferometry in telescope arrays assisted by quantum networks. In our approach, the quantum state of incoming photons along with an arrival time index are stored in a binary qubit code at each receiver. Nonlocal retrieval of the quantum state via entanglement-assisted parity checks at the expected photon arrival rate allows for direct extraction of the phase difference, effectively circumventing transmission losses between nodes. Compared to prior proposals, our scheme (based on efficient quantum data compression) offers an exponential decrease in required entanglement bandwidth. Experimental implementation is then feasible with near-term technology, enabling optical imaging of astronomical objects akin to well-established radio interferometers and pushing resolution beyond what is practically achievable classically.
Production and Evaluation of a Realistic Immersive Virtual Reality Organic Chemistry Laboratory Experience: Infrared Spectroscopy

Using virtual reality (VR) in educational settings is becoming increasingly popular. The feasibility of replacing an instrumentation-based organic chemistry lab with a VR experience has been evaluated. A VR laboratory experience was designed to teach students how to use an infrared spectrometer and elucidate an unknown structure from the resulting infrared spectrum. The resulting first-person VR experience is immersive and realistic, with a teaching assistant guiding the user along the steps required to complete the experiment, including feedback as needed. The VR experience was developed in WondaVR with selections made using gaze navigation. The resulting product was tested with a group of students, and the outcomes for short- and long-term recall were compared with a group of students that did the same experiment in a traditional lab. Results indicate that there are no significant differences in learning outcomes between the two groups, which indicates the possibility of using this tool to offer this organic chemistry lab experiment via distance education. Students that tried the VR experience reported a high degree of satisfaction with the product and no significant usability barriers. These VR experiences could be useful for students who are unable to be present in lab due to disabilities, attendance challenges such as pregnancy, or safety concerns.
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages

We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages.We then compare the performance of OSCARbased and Wikipedia-based ELMo embeddings for these languages on the part-ofspeech tagging and parsing tasks.We show that, despite the noise in the Common-Crawlbased OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia.They actually equal or improve the current state of the art in tagging and parsing for all five languages.In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the crosslingual benefit of multilingual embedding architectures.
Appearance-Motion Memory Consistency Network for Video Anomaly Detection

Abnormal event detection in the surveillance video is an essential but challenging task, and many methods have been proposed to deal with this problem. The previous methods either only consider the appearance information or directly integrate the results of appearance and motion information without considering their endogenous consistency semantics explicitly. Inspired by the rule humans identify the abnormal frames from multi-modality signals, we propose an Appearance-Motion Memory Consistency Network (AMMC-Net). Our method first makes full use of the prior knowledge of appearance and motion signals to explicitly capture the correspondence between them in the high-level feature space. Then, it combines the multi-view features to obtain a more essential and robust feature representation of regular events, which can significantly increase the gap between an abnormal and a regular event. In the anomaly detection phase, we further introduce a commit error in the latent space joint with the prediction error in pixel space to enhance the detection accuracy. Solid experimental results on various standard datasets validate the effectiveness of our approach.
Online Active Learning Ensemble Framework for Drifted Data Streams

In practical applications, data stream classification faces significant challenges, such as high cost of labeling instances and potential concept drifting. We present a new online active learning ensemble framework for drifting data streams based on a hybrid labeling strategy that includes the following: 1) an ensemble classifier, which consists of a long-term stable classifier and multiple dynamic classifiers (a multilevel sliding window model is used to create and update the dynamic classifiers to effectively process both the gradual drift type and sudden drift type data stream) and 2) active learning, which takes a nonfixed labeling budget, supports on-demand request labeling, and adopts an uncertainty strategy and random strategy to label instances. The decision threshold of the uncertainty strategy is adjusted dynamically, i.e., when concept drift occurs, the threshold is gradually reduced to query the most uncertain instances in priority to reduce the request expense as much as possible. Experiments on synthetic and real data sets show that precise prediction accuracy can be obtained by the proposed method without increasing the total cost of labeling, and that the labeling cost can be dynamically allocated according to the concept drift.
Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets

Nelson F. Liu, Roy Schwartz, Noah A. Smith. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes

Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction. In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities. As a proof-of-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on non-invasive sensors and deep neural networks. Moreover, the system provides an easy-to-interpret probability estimation for T2DM for a given patient. Besides the development of the AI, we further analyzed the acceptance of young people for AI in healthcare to estimate the impact of such system in the future.
Global entangling gates on arbitrary ion qubits

Quantum computers can efficiently solve classically intractable problems, such as the factorization of a large number1 and the simulation of quantum many-body systems2,3. Universal quantum computation can be simplified by decomposing circuits into single- and two-qubit entangling gates4, but such decomposition is not necessarily efficient. It has been suggested that polynomial or exponential speedups can be obtained with global N-qubit (N greater than two) entangling gates5-9. Such global gates involve all-to-all connectivity, which emerges among trapped-ion qubits when using laser-driven collective motional modes10-14, and have been implemented for a single motional mode15,16. However, the single-mode approach is difficult to scale up because isolating single modes becomes challenging as the number of ions increases in a single crystal, and multi-mode schemes are scalable17,18 but limited to pairwise gates19-23. Here we propose and implement a scalable scheme for realizing global entangling gates on multiple 171Yb+ ion qubits by coupling to multiple motional modes through modulated laser fields. Because such global gates require decoupling multiple modes and balancing all pairwise coupling strengths during the gate, we develop a system with fully independent control capability on each ion14. To demonstrate the usefulness and flexibility of these global gates, we generate a Greenberger-Horne-Zeilinger state with up to four qubits using a single global operation. Our approach realizes global entangling gates as scalable building blocks for universal quantum computation, motivating future research in scalable global methods for quantum information processing.
Integrating Blockchain Technology with Internet of things to Efficiency

The research study focused on integrating blockchain technology with the internet of things. The study is necessitated by the need to come up with practical and feasible means to improve the accuracy, accountability and trust among various parties involved in blockchain transactions. The research recognizes the fact that there has been an increasing using of the cryptocurrency in the global market. This has lead to the emergence of blockchain, which is one of the disruptive technologies in the information and technology sector. The research study indicates that there is a need to improve on the architecture of blockchain and the IoT system. It shows that there is a need to develop a new model to improve its efficiency and accuracy. This research paper recommends the use of decentralized model to enhance the efficiency of the integration of the blockchain technology and IoT.
Second-order statistics analysis and comparison between arithmetic and geometric average fusion: Application to multi-sensor target tracking

Two fundamental approaches to information averaging are based on linear and logarithmic combination, yielding the arithmetic average (AA) and geometric average (GA) of the fusing data, respectively. In the context of multi-sensor target tracking, the two most common formats of data to be fused are random variables and probability density functions, namely v-fusion and f-fusion, respectively. In this work, we analyze and compare the second-order statistics (including variance and mean square error) of AA and GA in terms of both v-fusion and f-fusion. The case of weighted Gaussian mixtures representing multitarget densities in the presence of false alarms and missed detections (whose weight sums are not necessarily unit) is also considered, the result of which turns out to be significantly different from that of a single target. In addition to exact derivation, exemplifying analyses and illustrations are also provided.