Reviewerly/exHarmony huggingface dataset:
Munec: a mutual neighbor-based clustering algorithm

It is expected for new clustering algorithms to find the appropriate number of clusters when dealing with complex data, meaning various shapes and densities. They also have to be self-tuning and adaptive for the input parameters to differentiate only between acceptable solutions. This work addresses this challenge. At the beginning mutual nearest neighbors are merged without any constraint until the number of groups including at least two items reaches a maximum. Subsequent mergings are only possible for mutual neighbor groups with a similar distance between neighbors. Finally, to manage more nuanced situations, heuristics that combine local density and distance are defined. The whole strategy aims to progressively consolidate the data representation structures. Munec requires some parameters. Most of them were integrated as constants and a single user parameter controls the process: the higher its value, the more constraints there are on the merging and the higher the number of clusters. Tests carried out using 2-dimensional datasets showed that Munec proved to be highly effective in matching a ground truth target. Moreover, with the same input configuration it can identify clusters of various densities, arbitrary shape and including a large amount of noise. These results hold for spaces of moderate dimension.
De jure, De facto Globalization and Economic Growth in Sub-Saharan Africa

Using data from 40 Sub-Saharan African (SSA) countries from 1980 to 2015, the present study examines the effects of globalization and its dimensions...
A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream. To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional (3-D) space, is a strategic problem due to its large-scale, complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.
Using spectrocolourimetry to trace sediment source dynamics in coastal catchments draining the main Fukushima radioactive pollution plume (2011–2017)

Spectrocolourimetric measurements provide a relatively inexpensive, quick and non-destructive alternative to the analysis of geochemical and organic matter properties. When used in the analysis of sediments and their potential sources, these colour parameters may provide important information on the dominant processes (i.e. erosion) occurring in the Critical Zone. Here, they are used to investigate whether eroded sediment is derived from forest (i.e. natural), cultivated (i.e. anthropogenic) or subsoil sources in order to assess their potential to monitor the effect of decontamination in regions impacted by fallout from the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident. Fifteen spectrocolourimetric properties (L*, a*, b*, C*, h, x, y, z, L, a, b, u*, v*, u', v') were measured in potential source (n = 37) and sediment (n = 400) samples collected during 13 campaigns from 2011 to 2017 after major flood events in two catchments (total surface area of 450 km2) draining the main FDNPP radioactive pollution plume. Potential sources included topsoil from forest and cultivated sources along with subsoil material originating from landslides, channel banks and the decontamination of cultivated areas. The optimum set of parameters used in the mixed linear model to calculate the sediment source contributions was obtained through the use of a range test, the Kruskal–Wallis H test and a linear discriminant analysis. Nine selected colour parameters correctly classified 100% of the source samples (i.e. forest, subsoil and cultivated sources). The results illustrate that cultivated landscapes were the main source of sediment to these river systems (mean 56%, SD 34%) followed by subsoil (mean 26%, SD 16%) and forest sources (mean 21%, SD 24%). However, these contributions varied strongly over time, with a peak of subsoil contributions (mean 57%, SD 17%) in Fall 2015, coinciding with the occurrence of a typhoon after the remediation works. These results were consistent with monitoring studies conducted in the same area that showed the major impact of typhoon Etau in September 2015 on sediment and radiocaesium fluxes. These original results demonstrate that spectrocolourimetric measurements may contribute to the routine monitoring of the effectiveness of remediation works in this post-accidental context. Owing to the inexpensive, rapid and non-destructive analyses, spectrocolourimetric-based tracing methods have significant potential to provide information on the dominant erosion processes occurring in the Critical Zone.
7.5 A 65nm 0.39-to-140.3TOPS/W 1-to-12b Unified Neural Network Processor Using Block-Circulant-Enabled Transpose-Domain Acceleration with 8.1 × Higher TOPS/mm<sup>2</sup>and 6T HBST-TRAM-Based 2D Data-Reuse Architecture

Energy-efficient neural-network (NN) processors have been proposed for battery-powered deep-learning applications, where convolutional (CNN), fully-connected (FC) and recurrent NNs (RNN) are three major workloads. To support all of them, previous solutions [1-3] use either area-inefficient heterogeneous architectures, including CNN and RNN cores, or an energy-inefficient reconfigurable architecture. A block-circulant algorithm [4] can unify CNN/FC/RNN workloads with transpose-domain acceleration, as shown in Fig. 7.5.1. Once NN weights are trained using the block-circulant pattern, all workloads are transformed into consistent matrix-vector multiplications (MVM), which can potentially achieve 8 to-128× storage savings and a O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-to-O(nlog(n)) computation complexity reduction.
Semideterministic Entanglement between a Single Photon and an Atomic Ensemble

Entanglement between a single photon and a matter qubit is an indispensable resource for quantum repeater and quantum networks. With atomic ensembles, the entanglement creation probability is typically very low to inhibit high-order events. In this paper, we propose and experimentally realize a scheme which creates atom-photon entanglement with an intrinsic efficiency of 50%. We make use of Rydberg blockade to generate two collective excitations, lying in separate internal states. By introducing the momentum degree of freedom for the excitations, and interfering them via Raman coupling, we entangle the two excitations. Via retrieving one excitation, we create the entanglement between the polarization of a single photon and the momentum of the remaining atomic excitation, with a measured fidelity of 0.901(8). The retrieved optical field is verified to be genuine single photons. The realized entanglement may be employed to create entanglement between two distant nodes in a fully heralded way and with a much higher efficiency.
How to Cope with Change? - Preserving Validity of Predictive Services over Time

Companies more and more rely on predictive services which are constantly monitoring and analyzing the available data streams for better service offerings.However, sudden or incremental changes in those streams are a challenge for the validity and proper functionality of the predictive service over time.We develop a framework which allows to characterize and differentiate predictive services with regard to their ongoing validity.Furthermore, this work proposes a research agenda of worthwhile research topics to improve the long-term validity of predictive services.In our work, we especially focus on different scenarios of true label availability for predictive services as well as the integration of expert knowledge.With these insights at hand, we lay an important foundation for future research in the field of valid predictive services.
Supervised learning based discrete hashing for image retrieval

Learning based hashing technologies have been widely adopted in multimedia retrieval as they could afford efficient storage and extract semantic information for high-dimensional data. However, the major difficulty of learning based hashing is the discrete constraint imposed on the required hashing codes, which makes the optimization generally to be NP-hard. In this paper, a novel supervised learning based discrete hashing (SLDH) approach is proposed to learn compact binary codes under the deep learning framework for image retrieval. We adopt multilayer network to convert the original features into binary codes, while it should exploit the semantic relevance of manual labels and keep the semantic similarity. For this purpose, we propose the objective function to obtain the binary codes including: 1) making full use of manual labels to get implicit semantic information; 2) using the weighted similarity matrix to keep the semantic similarity; 3) relaxing the discrete constraint to a normalized optimization problem; 4) adding the orthogonality constraint on binary codes to reduce the information redundancy. The objective function is optimized with the alternating direction method and modified alternating direction of multipliers(ADMM) algorithm with efficient iteration. Experiments are conducted on three databases and the results demonstrate the superiority to several state-of-the-art hashing based image retrieval methods.
OTLAMC: An Online Transfer Learning Algorithm for Multi-class Classification

Transfer learning handles a learning task on target domain by transferring the knowledge which was already learned from source domain, often under the situation where the labeled data in the target domain are insufficient or difficult to acquire. Transfer learning can be generally classified into two types, offline transfer learning and online transfer learning. As the former requires the data of target domain be given in advance, which may not be hold in real-life situations, the latter attracted more and more research in recent years. Online transfer learning deals with the situation where the data of target domain arrive in an online manner. Existing research on online transfer learning only deals with binary classification tasks, which appears to be important insufficiency. In this paper, the problem of online transfer learning for multi-class classification is studied, and an algorithm called Online Transfer Learning Algorithm for Multi-class Classification (OTLAMC) is proposed. OTLAMC learns a multi-class classifier in an online manner based on the knowledge from two sources, the obtained feedback of each datum in the target domain upon its arrival and the knowledge transferred from the source domain. A new loss function and a new updating method are proposed and adopted in OTLAMC, which improved the performance of OTLAMC on multi-class classification task. The mistake bound of OTLAMC is derived. Experiments on two widely used datasets illustrate that OTLAMC has good performance.
A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems

Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.
Casting to Corpus: Segmenting and Selecting Spontaneous Dialogue for Tts with a Cnn-lstm Speaker-dependent Breath Detector

This paper considers utilising breaths to create improved spontaneous-speech corpora for conversational text-to-speech from found audio recordings such as dialogue podcasts. Breaths are of interest since they relate to prosody and speech planning and are independent of language and transcription. Specifically, we propose a semi-supervised approach where a fraction of coarsely annotated data is used to train a convolutional and recurrent speaker-specific breath detector operating on spectrograms and zero-crossing rate. The classifier output is used to find target-speaker breath groups (audio segments delineated by breaths) and subsequently select those that constitute clean utterances appropriate for a synthesis corpus. An application to 11 hours of raw podcast audio extracts 1969 utterances (106 minutes), 87% of which are clean and correctly segmented. This outperforms a baseline that performs integrated VAD and speaker attribution without accounting for breaths.
Visually Encoding the Lived Experience of Bipolar Disorder

Issues of social identity, attitudes towards self-disclosure, and potentially biased approaches to what is considered "typical" or "normal" are critical factors when designing visualizations for personal informatics systems. This is particularly true when working with vulnerable populations like those who self-track to manage serious mental illnesses like bipolar disorder (BD). We worked with individuals diagnosed with BD to 1) better understand sense-making challenges related to the representation and interpretation of personal data and 2) probe the benefits, risks, and limitations of participatory approaches to designing personal data visualizations that better reflect their lived experiences. We describe our co-design process, present a series of emergent visual encoding schemas resulting from these activities, and report on the assessment of these speculative designs by participants. We conclude by summarizing important considerations and implications for designing personal data visualizations for (and with) people who self-track to manage serious mental illness.
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection

This article presents a hybrid methodology that combines Bayesian optimization (BO) with a constrained version of the GA-PARSIMONY method to obtain parsimony models. The proposal is designed to reduce the sizeable computational effort associated with the use of GA-PARSIMONY alone. The method begins with BO to obtain favorable initial model parameters. Then, with these parameters, a constrained GA-PARSIMONY is implemented to generate accurate parsimony models by using feature reduction, data transformation and parsimonious model selection. Experiments with extreme gradient boosting machines (XGBoost) and ten UCI databases demonstrated that the hybrid methodology obtains models analogous to those of GA-PARSIMONY while achieving significant reductions in elapsed time in eight out of ten datasets.
S<sup>3</sup>D: Scalable Pedestrian Detection via Score Scale Surface Discrimination

Pedestrian detection has remained an important research topic in both the computer vision and multimedia communities because of its importance in practical applications, such as driving assistance and video surveillance. Existing methods compare the response score with a fixed threshold to determine whether a candidate region contains pedestrians and produce dissatisfactory results that contain either missed detections or false detections, which are difficult to balance. This situation has a serious impact under the condition of variable scale. This paper investigates the functional relationship between the scores and scales of pedestrians. By designing experiments with multiple scales, we have found a discriminant surface in the score scale space. Pedestrians can be distinguished at various scale levels according to their locations on the discriminant surface. The proposed approach is evaluated using four challenging pedestrian detection datasets, including Caltech, INRIA, ETH, and KITTI, and the superior experimental results are achieved when compared with baseline methods.
Accurate depth estimation in structured light fields

Passive light field imaging generally uses depth cues that depend on the image structure to perform depth estimation, causing robustness and accuracy problems in complex scenes. In this study, the commonly used depth cues, defocus and correspondence, were analyzed by using phase encoding instead of the image structure. The defocus cue obtained by spatial variance is insensitive to the global spatial monotonicity of the phase-encoded field. In contrast, the correspondence cue is sensitive to the angular variance of the phase-encoded field, and the correspondence responses across the depth range have single-peak distributions. Based on this analysis, a novel active light field depth estimation method is proposed by directly using the correspondence cue in the structured light field to search for non-ambiguous depths, and thus no optimization is required. Furthermore, the angular variance can be weighted to reduce the depth estimation uncertainty according to the phase encoding information. The depth estimation of an experimental scene with rich colors demonstrated that the proposed method could distinguish different depth regions in each color segment more clearly, and was substantially improved in terms of phase consistency compared to the passive method, thus verifying its robustness and accuracy.
Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging

Lifu Huang, Heng Ji, Jonathan May. 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.
Cooperative unmanned aerial vehicles with privacy preserving deep vision for real-time object identification and tracking

Human tracking is an important challenge in a wide variety of applications, including but not limited to, surveillance, military operations, and disaster relief services. Unmanned Aerial Vehicles (UAVs) allow the surveying of dangerous or impassable areas from a safe distance. They also provide a machine-based capability, which may not only solve resource constraint issues, but can also improve effectiveness and efficiency in the tracking task. The effectiveness of tracking is directly related to the angle of view and degree of freedom of the camera system. In this paper, we introduce a decentralized, distributed deep learning algorithm for Real-Time Privacy-preserving Target Tracking Re-Identification (RPTT-ReID) used by cooperative UAVs in complex and adversarial environments involving motion, crowded scenes, and varied camera angles. The efficiency of RPTT-ReID makes it amenable to edge computing applications. The proposed algorithmic approach resolves shortfalls with current tracking algorithms, specifically challenges in maintaining tracking when subjects cross paths, switch identity, or are occluded in a frame of view. We demonstrate the power of our approach both in single and multi-UAV scenarios to track movable targets by extracting the facial embedding information in crowds, in order to ensure the privacy of individuals captured by the UAVs without compromising the capability for target re-identification. We validate RPTT-ReID on a challenging video dataset of crowded scenes. Our experimental evaluation shows that the proposed approach is capable of tracking and re-identifying people in crowds despite blended trajectories with minimum and maximum accuracy of 79.91 ± 0.2% and 93.27 ± 0.1% respectively. The proposed approach is 18% faster than previous methods for tracking in crowded urban environments.
VLSI implementation of reversible logic gates cryptography with LFSR key

The reversible computation is an emerging research field, which is used in different applications such as optical computing, digital signal processing, nanotechnologies, bio-information and in low-power computation. In present days, these applications require security algorithm to keep the information securely. The power and area analysis are one of the major challenges to mathematically secure cryptography protocols. Moreover, hackers can take confidential data while it passes through the transmission line. To overcome this problem, Reversible logic cryptography design (RLCD) architecture is introduced in this paper. With the help of RLCD, the encryption and decryption architecture is designed. Linear feedback shift register (LFSR) is required to produce a key which is given to the encryption and decryption block. The application specified integrated chip (ASIC) and field-programmable gate array (FPGA) performances are evaluated for both existing and proposed method. More than 7% of the ASIC performances improved in RLCD-LFSR method compared to the conventional methods.
Co-design in mental health; Mellow: a self-help holistic crisis planning mobile application by youth, for youth

(2019). Co-design in mental health; Mellow: a self-help holistic crisis planning mobile application by youth, for youth. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1529-1542.