Reviewerly/exHarmony huggingface dataset:
Cascade of One Class Classifiers for Water Level Anomaly Detection

Intelligent anomaly detection is a promising area to discover anomalies as manual processing by human are generally labor-intensive and time-consuming. An effective approach to deal with is essentially to build a classifier system that can reflect the condition of the infrastructure when it tends to behave abnormally, and therefore the appropriate course of action can be taken immediately. In order to achieve aforementioned objective, we proposed to build a dual-staged cascade one class SVM (OCSVM) for water level monitor systems. In the first stage of the cascade model, our OCSVM learns directly on single observation at a time, 1-g to detect point anomaly. Whereas in the second stage, OCSVM learns from the constructed n-gram feature vectors based on the historical data to discover any collective anomaly where the pattern from the n-gram failed to conform to the expected normal pattern. The experimental result showed that our proposed dual-staged OCSVM is able to detect anomaly and collective anomalies effectively. Our model performance has attained remarkable result of about 99% in terms of F1-score. We also compared the performance of our OCSVM algorithm with other algorithms.
Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

Integration of reinforcement learning and imitation learning is an important problem that has been studied for a long time in the field of intelligent robotics. Reinforcement learning optimizes policies to maximize the cumulative reward, whereas imitation learning attempts to extract general knowledge about the trajectories demonstrated by experts, i.e., demonstrators. Because each of them has their own drawbacks, methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of the methods are heuristic and do not have a solid theoretical basis. In this paper, we present a new theory for integrating reinforcement and imitation learning by extending the probabilistic generative model framework for reinforcement learning, {\it plan by inference}. We develop a new probabilistic graphical model for reinforcement learning with multiple types of rewards and a probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of reinforcement learning and imitation learning can be formulated as a probabilistic inference of policies on pMDP-MO by considering the output of the discriminator in generative adversarial imitation learning as an additional optimal emission observation. We adapt the generative adversarial imitation learning and task-achievement reward to our proposed framework, achieving significantly better performance than agents trained with reinforcement learning or imitation learning alone. Experiments demonstrate that our framework successfully integrates imitation and reinforcement learning even when the number of demonstrators is only a few.
Solving optimization problems with Rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks

Platforms of Rydberg atoms have been proposed as promising candidates to solve some combinatorial optimization problems. Here, we compute quantitative requirements on the system sizes and noise levels that these platforms must fulfill to reach quantum advantage in approximately solving the Unit-Disk Maximum Independent Set problem. Using noisy simulations of Rydberg platforms of up to 26 atoms interacting through realistic van der Waals interactions, we compute the average approximation ratio that can be attained with a simple quantum annealing-based heuristic within a fixed temporal computational budget. Based on estimates of the correlation lengths measured in the engineered quantum state, we extrapolate the results to large atom numbers and compare them to a simple classical approximation heuristic. We find that approximation ratios of at least $\approx 0.84$ are within reach for near-future noise levels. Not taking into account further classical and quantum algorithmic improvements, we estimate that quantum advantage could be reached by attaining a number of controlled atoms of $\sim8,000$ for a time budget of 2 seconds, and $\sim 1,000-1,200$ for a time budget of 0.2 seconds, provided the coherence levels of the system can be improved by a factor 10 while maintaining a constant repetition rate.
QoS Parameters for Viable SLA in Cloud

Quality of Service (QoS) is a key factor for a viable Service Level Agreement (SLA) which ensures the formation of a reliable provider-consumer relationship in cloud computing. SLA ties up a service provider with a service consumer on agreed upon terms and conditions so that the provider’s promised services and the consumer’s requirements are fulfilled which results in a trustful relationship between them. In this paper, an analysis of the existing literature is made to identify the gaps needed to be filled for establishing a trustful relationship. The literature is classified w.r.t the QoS potential attributes i.e. trust, resource management, risk management, security, task scheduling and performance etc. contributing to maintain a feasible SLA.
LightBulb: A Photonic-Nonvolatile-Memory-based Accelerator for Binarized Convolutional Neural Networks

Although Convolutional Neural Networks (CNNs) have demonstrated the state-of-the-art inference accuracy in various intelligent applications, each CNN inference involves millions of expensive floating point multiply-accumulate (MAC) operations. To energy-efficiently process CNN inferences, prior work proposes an electro-optical accelerator to process power-of-2 quantized CNNs by electro-optical ripple-carry adders and optical binary shifters. The electro-optical accelerator also uses SRAM registers to store intermediate data. However, electro-optical ripple-carry adders and SRAMs seriously limit the operating frequency and inference throughput of the electro-optical accelerator, due to the long critical path of the adder and the long access latency of SRAMs. In this paper, we propose a photonic nonvolatile memory (NVM)-based accelerator, Light-Bulb, to process binarized CNNs by high frequency photonic XNOR gates and popcount units. LightBulb also adopts photonic racetrack memory to serve as input/output registers to achieve high operating frequency. Compared to prior electro-optical accelerators, on average, LightBulb improves the CNN inference throughput by 17× ~ 173× and the inference throughput per Watt by 17.5 × ~ 660×.
Removal of Hg interferences for common Pb correction when dating apatite and titanite by LA-ICP-MS/MS

Improved <sup>204</sup>Pb common Pb correction by LA-ICP-MS/MS for apatite and titanite U-Pb dating.
Cross-modal Variational Alignment of Latent Spaces

In this paper, we propose a novel cross-modal variational alignment method in order to process and relate information across different modalities. The proposed approach consists of two variational autoencoder (VAE) networks which generate and model the latent space of each modality. The first network is a multi modal variational autoencoder that maps directly one modality to the other, while the second one is a single-modal variational autoencoder. In order to associate the two spaces, we apply variational alignment, which acts as a translation mechanism that projects the latent space of the first VAE onto the one of the single-modal VAE through an intermediate distribution. Experimental results on four well-known datasets, covering two different application domains (food image analysis and 3D hand pose estimation), show the generality of the proposed method and its superiority against a number of state-of-the-art approaches.
CrowdUI: Supporting Web Design with the Crowd

Web design is a complex and challenging task. It involves making many design decisions that materialise preconceived notions of user needs that may or may not be true. In this paper, we investigate supporting the co-design of a website with visual feedback elicited from the website's community of users. Website users can express their needs by re-arranging and modifying the website's layout and design. To explore and validate this idea, we present CrowdUI, a web-based tool that enables members of the community of a website to visually express their design improvement ideas, frustrations and needs, and to send this feedback to the person in charge of designing or maintaining the website. CrowdUI is validated in a study with 45 users of a popular social media and networking website. Second, our qualitative evaluation with 60 experienced web developers shows that CrowdUI is able to elicit diverse and meaningful feedback. Put together, our results suggest that CrowdUI's approach constitutes a productive setting for eliciting visual feedback from the user community as a complement to traditional ways of eliciting feedback and participatory design. Finally, based on our experiences, we discuss a design space for crowdsourced web design and provide design recommendations for similar future tools.
DSRPH: Deep semantic-aware ranking preserving hashing for efficient multi-label image retrieval

In the recent years, several hashing methods have been proposed for multi-label image retrieval. However, general methods quantify the similarities of image pairs roughly, which only consider the similarities based on category labels. In addition, general pairwise loss functions are not sensitive to the relative order of similar images. To address above problems, we present a deep semantic-aware ranking preserving hashing (DSRPH) method. First, we design a semantic-aware similarity quantization method which can measure fine-grained semantic-level similarity beyond the category based on the cosine similarity of image captions that contain high-level semantic description. Second, we propose a novel weighted pairwise loss function by adding adaptive upper and lower bounds, which can construct a compact zero-loss interval to directly constrain the relative order of similar images. Extensive experiments show that our method can generate high-quality hash codes and yield the state-of-the-art performance.
Extracting temporal and causal relations based on event networks

Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods.
Location-based data access control scheme for Internet of Vehicles

Internet of Vehicles (IoV) is a typical representative of the Internet of Things in the area of transportation systems. Due to the mobility of vehicles, most applications for IoV are based on the location information. How to provide location-based access control is especially important for the IoV environment. Based on the multi-authority attribute-based encryption, we propose a novel location-based encryption scheme that enables both attribute-based and location-based access control for cloud storage. In this scene, vehicles can access data only if they arrive at a designated location and their attributes satisfy the access policy simultaneously. In the deployment of our scheme, we introduce fog computing and blockchain technologies. We deploy the fog nodes in the data access area to issue location-based keys and aid decrypting for vehicles, while we deploy the blockchain system to publish public parameters. The experiment shows that our scheme is feasible in practical applications.
A novel feature representation: Aggregating convolution kernels for image retrieval

Activated hidden units in convolutional neural networks (CNNs), known as feature maps, dominate image representation, which is compact and discriminative. For ultra-large datasets, high dimensional feature maps in float format not only result in high computational complexity, but also occupy massive memory space. To this end, a new image representation by aggregating convolution kernels (ACK) is proposed, where some convolution kernels capturing certain patterns are activated. The top-n index numbers of the convolution kernels are extracted directly as image representation in discrete integer values, which rebuild relationship between convolution kernels and image. Furthermore, a distance measurement is defined from the perspective of ordered sets to calculate position-sensitive similarities between image representations. Extensive experiments conducted on Oxford Buildings, Paris, and Holidays, etc., manifest that the proposed ACK achieves competitive performance on image retrieval with much lower computational cost, outperforming the ones using feature maps for image representation.
Terrain-Aware Path Planning and Map Update for Mars Sample Return Mission

This work aims at developing an efficient path planning algorithm for the driving objective of a Martian day (sol) that can take into account terrain information for application to the proposed Mars Sample Return (MSR) mission. To prepare the planning process for one sol (i.e., with a limited time allocated to driving), a map of expected rover velocity over a chosen area is constructed, obtained by combining traversability classes, rock abundance and slope at that location. The planning phase starts offline by computing several potential paths that can be traversed in one sol (i.e., a few hours), which will later provide suitable options to the rover if replanning is necessary due to unexpected mobility difficulties. Online, the rover gains information about its environment as it drives and updates the map locally if major discrepancies are found. If an update is made, the remaining driving time along the different options is recalculated and the most efficient path is chosen. The online process is repeated until the rover has reached its daily goal. When simulated on different areas at Gusev Crater, Mars, the algorithm correctly captured changes of terrain initially not mapped, and rerouted the rover to a more efficient path when necessary, in which case it effectively complied with the time constraint to reach the goal.
Road Anomaly Detection Through Deep Learning Approaches

This paper addresses road anomaly detection by formulating it as a classification problem and applying deep learning approaches to solve it.Besides conventional road anomalies, additional ones are introduced from the perspective of a vehicle.In order to facilitate the learning process, the paper pays a close attention to pattern representation, and proposes three sets of numeric features for representing road conditions.Also, three deep learning approaches, i.e.Deep Feedforward Network (DFN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), are considered to tackle the classification problem.The detectors, with respect to the three deep learning approaches, are trained and evaluated through data collected from a test vehicle driven on various road anomaly conditions.The comparison study on the detection performances is conducted by setting key hyper-parameters to certain sets of fixed values.Also, the comparison study on performances of each detector with respect to different pattern representations is conducted.The results have shown the effectiveness of the proposed approaches and the efficiency of the proposed feature representations in road anomaly detection.
Distractor-aware discrimination learning for online multiple object tracking

Online multi-object tracking needs to overcome the intrinsic detector deficiencies, e.g., missing detections, false alarms, and inaccurate detection responses, to grow multiple object trajectories without using future information. Various distractions exist during this growing process like background clutters, similar targets, and occlusions, which present a great challenge. We in this work propose a method for learning a distractor-aware discriminative model that can handle continuous missed and inaccurate detection problems due to the occlusion or the motion blur. To deal with target appearance variations, a relational attention learning mechanism is proposed to capture the distinctive target appearances by selectively aggregating features from history states with weights extracted from their appearance topological relationship. Based on the discrimination model, a multi-stage tracking pipeline is designed for automatic trajectory initialization,propagation, and termination. Extensive experimental analyses and comparisons demonstrate its state-of-the-art performance on widely used challenging MOT16 and MOT17 benchmarks. The source code of this work is released to facilitate further studies on the multi-object tracking problem.1
iDEC

Approximate Nearest Neighbor (ANN) search is a fundamental algorithmic problem, with numerous applications in many areas of computer science. In this work, we propose indexable distance estimating codes (iDEC) , a new solution framework to ANN that extends and improves the locality sensitive hashing (LSH) framework in a fundamental and systematic way. Empirically, an iDEC-based solution has a low index space complexity of O ( n ) and can achieve a low average query time complexity of approximately O (log n ). We show that our iDEC-based solutions for ANN in Hamming and edit distances outperform the respective state-of-the-art LSH-based solutions for both in-memory and external-memory processing. We also show that our iDEC-based in-memory ANN-H solution is more scalable than all existing solutions. We also discover deep connections between Error-Estimating Codes (EEC), LSH, and iDEC.
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages

A large percentage of the world's population speaks a language of the Indian subcontinent, comprising languages from both Indo-Aryan (e.g.Hindi, Punjabi, Gujarati, etc.) and Dravidian (e.g.Tamil, Telugu, Malayalam, etc.) families.A universal characteristic of Indian languages is their complex morphology, which, when combined with the general lack of sufficient quantities of high-quality parallel data, can make developing machine translation (MT) systems for these languages difficult.Neural Machine Translation (NMT) is a rapidly advancing MT paradigm and has shown promising results for many language pairs, especially in large training data scenarios.Since the condition of large parallel corpora is not met for Indian-English language pairs, we present our efforts towards building efficient NMT systems between Indian languages (specifically Indo-Aryan languages) and English via efficiently exploiting parallel data from the related languages.We propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related language pairs.We also propose a Multilingual Transfer Learning technique to leverage parallel data from multiple related languages to assist translation for lowresource language pair of interest.Our experiments demonstrate an overall average improvement of 5 BLEU points over the standard Transformer-based NMT baselines.
EmHash: Hashtag Recommendation using Neural Network based on BERT Embedding

Social media like Twitter have become very popular in recent decades. Hashtags are new kind of metadata which make non-structured tweets into searchable semistructured content. There are varied previous methods which recommend hashtags for new tweets. However, to the best of our knowledge, there is no previous word that uses BERT embedding for this purpose. In this paper, we propose a new method called EmHash that uses neural network based on BERT embedding to recommend new hashtags for each tweet. Unlike other word embeddings, BERT embedding constructs different vectors for the same word in different contexts. Emhash succeeded in outperforming three methods LDA, SVM, and TTM with respect to recall measure.
Generation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -qubit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> states using spin torque

We examine here a scheme to generate a W state of an n-qubit system with all-to-all pairwise exchange interaction between n qubits. This relies on sharing of superposed excitations of a smaller number of $q$ qubits among others. We present a bound on the maximal jumps from q to n and formalize a scheme to generate $W_n$ state in $\mathcal{O}(\log_4 n)$ stages. We demonstrate this scheme in the context of spin torque based quantum computing architecture that are characterized by repeated interactions between static and flying qubits.
Multilabel naïve Bayes classification considering label dependence

Multilabel classification is the task of assigning relevant labels to an instance, and it has received considerable attention in recent years. This task can be performed by extending a single-label classifier, such as the naïve Bayes classifier, to utilize the useful relations among labels for achieving better multilabel classification accuracy. However, the conventional multilabel naïve Bayes classifier treats each label independently and hence neglects the relations among labels, resulting in degenerated accuracy. We propose a new multilabel naïve Bayes classifier that considers the relations or dependence among labels. Experimental results show that the proposed method outperforms conventional multilabel classifiers.
Functional Encryption for Attribute-Weighted Sums from k-Lin

We present functional encryption schemes for attribute-weighted sums, where encryption takes as input N attribute-value pairs $$(x_i,z_i)$$ where $$x_i$$ is public and $$z_i$$ is private; secret keys are associated with arithmetic branching programs f, and decryption returns the weighted sum $$\sum _{i=1}^N f(x_i) z_i$$ while leaking no additional information about the $$z_i$$ ’s. Our main construction achieves