Reviewerly/exHarmony huggingface dataset:
What can AI do for me?

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We propose an evaluation of interpretation on a real task with real human users, where the effectiveness of interpretation is measured by how much it improves human performance. We design a grounded, realistic human-computer cooperative setting using a question answering task, Quizbowl. We recruit both trivia experts and novices to play this game with computer as their teammate, who communicates its prediction via three different interpretations. We also provide design guidance for natural language processing human-in-the-loop settings.
Mutual information based multi-label feature selection via constrained convex optimization

Multi-label learning has been extensively studied in many areas such as information retrieval, bioinformatics, and multimedia annotation. However, multi-label datasets often have noisy, irrelevant and redundant features with high dimensionality. Accompanying with these issues, a critical challenge is known as the curse of dimensionality. As an effective data preprocessing method, feature selection has received much attention for that it can provide a way in reducing computation time, improving prediction performance and enhancing understanding of the data. Based on this, a large number of information-theoretical-based feature selection methods are developed to solve the learning problem, i.e. multi-label classification. Unfortunately, most of existing information-theoretical-based feature selection methods are either directly transformed from single-label feature selection methods or insufficient in light of using heuristic algorithms as the search component. Motivated by this, in this paper, we propose a novel mutual-information-based feature selection method, which obtains the optimal solution via constrained convex optimization with less time. Specifically, by incorporating the label information into the feature selection process, label-correlation is taken into consideration to generate the generalized model. Finally, the experimental results on various multi-label datasets demonstrate the effectiveness and efficiency of the proposed framework.
A Novel 3-D Path Extraction Method for Arc Welding Robot Based on Stereo Structured Light Sensor

With the rapid development of computer vision and industrial technology, the requirements of the intelligent welding robots are increasing in the real industrial production. The traditional teaching-playback mode and the off-line programming mode cannot meet the automation demand and self-adaptive ability of welding robots. In order to improve the efficiency of welding robots, this paper proposes a novel 3-D path extraction method of weld seams based on a stereo-structured light sensor. Faced with the low efficiency of the line-structured light and the poor robustness of passive vision, the seam extraction based on a point cloud processing algorithm is proposed which could well adapt to the weld seams with different types and different groove sizes. Meanwhile, the position information and pose information of weld seam are established to serve 3-D path teaching of a welding robot. The experimental results show that the maximum path extraction error of V-type butt joint is less than 0.7 mm. The proposed scheme could well serve for the 3-D path teaching task before welding.
Multi-resolution attention convolutional neural network for crowd counting

Estimating crowd counts remains a challenging task due to the problems of scale variations, non-uniform distribution and complex backgrounds. In this paper, we propose a multi-resolution attention convolutional neural network (MRA-CNN) to address this challenging task. Except for the counting task, we exploit an additional density-level classification task during training and combine features learned for the two tasks, thus forming multi-scale, multi-contextual features to cope with the scale variation and non-uniform distribution. Besides, we utilize a multi-resolution attention (MRA) model to generate score maps, where head locations are with higher scores to guide the network to focus on head regions and suppress non-head regions regardless of the complex backgrounds. During the generation of score maps, atrous convolution layers are used to expand the receptive field with fewer parameters, thus getting higher-level features and providing the MRA model more comprehensive information. Experiments on ShanghaiTech, WorldExpo’10 and UCF datasets demonstrate the effectiveness of our method.
Developing a gamified mobile application to encourage sustainable energy use in the office

Faced with growing pressures to become more environmentally sustainable, many companies are exploring innovative ways to incorporate "green" practices into their business processes. We focus on employees and their pro-environmental behaviours in the workplace. Drawing on gamification and persuasive design principles, we utilized five design cycles to develop and test a system that tracks employees' electricity usage on their computer-related equipment, engages them through a mobile application using a garden metaphor, and encourages them to reduce their energy consumption. The results of the design cycles built on each other, demonstrating that the system decreases employees' electricity consumption and increases their motivation to continue engaging in pro-environmental behaviours. Possible extensions to the system were also explored. Reflecting on our experiences, seven guidelines emerged related to gamification design and the wider field of design science research. Limitations and future research directions for gamification and environmental sustainability research are discussed.
Semi-Supervised Feature Selection via Sparse Rescaled Linear Square Regression

With the rapid increase of the data size, it has increasing demands for selecting features by exploiting both labeled and unlabeled data. In this paper, we propose a novel semi-supervised embedded feature selection method. The new method extends the least square regression model by rescaling the regression coefficients in the least square regression with a set of scale factors, which is used for evaluating the importance of features. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a sparse model with a flexible and adaptable ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2;p</sub> norm regularization. Moreover, the optimal solution of scale factors provides a theoretical explanation for why we can use {||w <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> || <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , . . .,||w <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> || <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> } to evaluate the importance of features. Experimental results on eight benchmark data sets show the superior performance of the proposed method.
Bitcoin and the day-of-the-week effect

The day-of-the-week effect is a well-known phenomenon in financial markets, detected in the price of equities, bonds, currencies and commodities. In this study, we extend the exploration of this anomaly to Bitcoin. Using OLS and GARCH models with daily data for 2010–2017, we provide initial evidence about the existence of the day-of-the-week effect anomaly not only in returns but also in the volatility of Bitcoin. Our results also indicate the strong independence of Bitcoin and that classic speculative variables in the financial markets are limited in forecasting the price of Bitcoin. Our results are robust using different subsamples, estimation procedures and control variables.
Communication through quantum-controlled noise

In a recent series of works [Ebler et al., Phys. Rev. Lett. 120, 120502 (2018); arXiv:1809.06655v2; arXiv:1810.10457v2], it has been shown that the quantum superposition of causal order---the quantum switch---offers an enhancement of classical and quantum channel capacity through noisy channels, a phenomenon that was coined ``causal activation.'' In this paper, we attempt to clarify the nature of the advantage, by comparing the quantum switch to a class of processes that can be interpreted as quantum superposition of processes with the same causal order. We show that some of these processes can match or even outperform the quantum switch at enhancing classical and quantum channel capacity, and argue that they require the same resources as the switch. We conclude, in agreement with Abbott et al. [arXiv:1810.09826v1], that the aforementioned advantages appear to be attributable to the ability to coherently control quantum operations, and not to indefinite causal order per se.
Multi-Access Edge Computing Enhanced Video Streaming: Proof-of-Concept Implementation and Prediction/QoE Models

ETSI multi-access edge computing (MEC) provides an IT service environment and cloud-computing capabilities at the edge of the mobile network, enabling application and content providers to deploy new use cases, such as intelligent video acceleration, with low latency and high bandwidth. Specifically, ETSI MEC introduces an MEC server that implements the edge-cloud platform to host partial server-side service logics in the form of MEC applications (MEC Apps). In this paper, we aim to implement the first proof-of-concept (PoC) in the literature for the MEC-enhanced mobile video streaming service. Our PoC consists of Android User Apps, an MEC App, and the YouTube server. The MEC App implements two main functions: popular video caching and radio analytics/video quality adaptation. The User App provides general functions of a YouTube video streaming app and can access the videos from the cache server or the YouTube server under the MEC server's guidance. In addition to the PoC implementation, this paper further develops two machine learning models to be incorporated into the MEC App for popular video prediction and radio channel quality prediction, which allows to consider the effect of non-negligible round-trip times and adjust the video quality more accurately. The experimental results justify that our models, together with other advantages from MEC, can guarantee good performance for the mobile video streaming service. Finally, we model and investigate the effectiveness of the MEC architecture for improving the quality of experience of video-streaming users.
BBARS: Blockchain-Based Anonymous Rewarding Scheme for V2G Networks

In vehicle-to-grid (V2G) networks, battery-powered vehicle (BV) provides service to the power grid. In order to encourage more BVs to provide the service for power grid, it is necessary to reward the BVs from the power grid. To extensively deploy V2G networks, some security and privacy problems must be solved. In this paper, for the first time, we propose the novel concept of blockchain-based anonymous rewarding scheme (BBARS) for V2G networks. The novel concept comes from the application requirement which has not been solved by now. We give the formal system model and security model of BBARS. Then, we design the concrete BBARS scheme by making use of two different public key cryptosystem. Through security analysis and performance analysis, the designed scheme is provably secure and efficient. The analysis results also show the designed BBARS scheme is practical for secure V2G networks in smart grid.
Mining local and peak high utility itemsets

A major limitation of traditional High Utility Itemset Mining (HUIM) algorithms is that they do not consider that the utility of itemsets may vary over time. Thus, traditional HUIM algorithms cannot find itemsets that do not yield a high utility when considering the whole database, but still have a high utility during specific time periods. Discovering such itemsets is useful, as a product may sell exceptionally well during specific time periods but not during the rest of the year. This paper addresses this limitation of HUIM by defining the problem of mining local high utility itemsets (LHUI), and an extension to mine peak high utility itemsets (PHUI), which consists of finding the time periods where an itemset generates a utility that is much higher than usual (a peak). Algorithms named LHUI-Miner and PHUI-Miner are proposed to mine these patterns. Moreover, because the set of PHUIs can be large and some items in PHUIs don’t contribute much to their peaks, a third algorithm named NPHUI-Miner is proposed to discover a smaller set of patterns called Non-redundant Peak High Utility Itemsets (NPHUIs). Experimental results show that the proposed algorithms are efficient and can find useful patterns.
Online sexual harassment over anonymous social media in Bangladesh

Prior research on anonymous social media (ASM) has studied the issue of sexual harassment and has revealed its connections to stereotyping, aggression, interpersonal relationships, and mental health among others [16, 24, 60]. However, the characteristics of such harassment in the context of low and middle-income countries (LMICs) in the global south has not received enough attention in the literature. This paper presents our findings on the use of ASM in Bangladesh based on an anonymous online survey of (n= 291) participants and semi-structured interviews with (n= 27) participants. Our study shows a wide prevalence of sexual harassment on anonymous social networks in Bangladesh, the relationship between a closely-knitted communal culture and anonymous harassment, and the lack of infrastructural support for the victims. We also propose a set of design and policy recommendations for such anonymous social media to extend the current ICTD literature on ensuring a safer online environment for women, especially in an LMIC.
Deep Learning Based Radio-Signal Identification With Hardware Design

This paper proposes a deep learning based intelligent method for detecting and identifying radio signals considering two applications: first, cognitive radar for identifying micro unmanned aerial systems and second, an automated modulation classification scheme for cognitive radio, which can be used for aeronautical communication systems. Our proposed intelligent method is designed of a spectral correlation function based feature extractor and a low-complexity deep belief network classifier with low FPGA logic utilization.
Stochastic configuration networks with block increments for data modeling in process industries

Stochastic configuration networks (SCNs) that employ a supervisory mechanism to automatically and fast construct universal approximators can achieve promising performance for resolving regression problems. This paper develops an extension of the original SCNs with block increments to enhance learning efficiency, which has received considerable attention in industrial process modeling. This extension allows the learner model to add multiple hidden nodes (termed hidden node block) simultaneously to the network during construction process. To meet industrial demands, two block incremental implementations of SCNs are presented by adopting different strategies for setting the block size. Specifically, the first one adds the hidden node blocks with a fixed block size, which achieves the acceleration of convergence rate at the cost of model compactness; the second one can automatically set the block size by incorporating simulated annealing algorithm, achieving a good balance between efficiency and complexity. The two algorithms are suitable for industrial data modeling with distinct requirements on modeling speed and memory space. The improved methods for building SCNs are evaluated by two function approximations, four benchmark datasets and two real world applications in process industries. Experimental results with comparisons indicate that the proposed schemes perform favorably.
OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions

Deep networks have set the state-of-the-art in most image analysis tasks by replacing handcrafted features with learned convolution filters within end-to-end trainable architectures. Still, the specifications of a convolutional network are subject to much manual design - the shape and size of the receptive field for convolutional operations is a very sensitive part that has to be tuned for different image analysis applications. 3D fully-convolutional multi-scale architectures with skip-connection that excel at semantic segmentation and landmark localisation have huge memory requirements and rely on large annotated datasets - an important limitation for wider adaptation in medical image analysis. We propose a novel and effective method based on trainable 3D convolution kernels that learns both filter coefficients and spatial filter offsets in a continuous space based on the principle of differentiable image interpolation first introduced for spatial transformer network. A deep network that incorporates this one binary extremely large and inflecting sparse kernel (OBELISK) filter requires fewer trainable parameters and less memory while achieving high quality results compared to fully-convolutional U-Net architectures on two challenging 3D CT multi-organ segmentation tasks. Extensive validation experiments indicate that the performance of sparse deformable convolutions is due to their ability to capture large spatial context with few expressive filter parameters and that network depth is not always necessary to learn complex shape and appearance features. A combination with conventional CNNs further improves the delineation of small organs with large shape variations and the fast inference time using flexible image sampling may offer new potential use cases for deep networks in computer-assisted, image-guided interventions.
Link Prediction on N-ary Relational Data

With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to complete KGs for a long time. However, they mainly focus on promoting the performance on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction concerning more than two arities. It has even proven that they may cause loss of structural information. To overcome these problems, in this paper, without decomposition, we represent each n-ary relational fact as a set of its role-value pairs. We further propose a method to conduct Link Prediction on N-ary relational data, thus called NaLP, which explicitly models the relatedness of all the role-value pairs in the same n-ary relational fact. Experimental results validate the effectiveness and merits of the proposed NaLP method.
Investigating the use of random forest in software effort estimation

Over the last two decades, there has been an important increase in studies dealing with the software development effort estimation (SDEE) using machine learning (ML) techniques that aimed to improve the accuracy of the estimates and to understand the process used to generate these estimates. Among these ML techniques, decision tree-based models have received a considerable scholarly attention thanks to their generalization ability and understandability. However, very few studies have investigated the use of random forest (RF) in software effort estimation. In this paper, a RF model is designed and optimized empirically by varying the values of its key parameters. The performance of the RF is compared with that of classical regression tree (RT). The evaluation was performed through the 30% hold-out validation method using three datasets: ISBSG R8, Tukutuku and COCOMO. To identify the most accurate techniques, we used three widely known accuracy measures: Pred(0.25), MMRE and MdMRE. The results show that the optimized random forest outperforms the regression trees model on all evaluation criteria.
Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators

Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency fingerprint. This paper presents an approach for identifying emitters using convolutional neural networks to estimate the inphase/quadrature (IQ) imbalance parameters of each emitter, using only the received raw IQ data as input. Because an emitter's IQ imbalance parameters will not change as it changes modulation schemes, the proposed approach has the ability to track emitters, even as they change the modulation scheme. The performance of the developed approach is evaluated using simulated quadrature amplitude modulation and phase-shift keying signals, and the impact of signal-to-noise ratio, imbalance value, and modulation scheme are considered. Furthermore, the developed approach is shown to outperform a comparable feature-based approach, while making fewer assumptions and using fewer data per decision.
Using Compact Evolutionary Tabu Search algorithm for matching sensor ontologies

To implement the semantic interoperability among intelligent sensor applications, it is necessary to match the identical entities across the sensor ontologies. Since sensor ontology matching problem requires matching thousands of sensor concepts and has many local optimal solutions, Evolutionary Algorithm (EA) becomes the state-of-the-art methodology for solving it. However, the premature convergence and long runtime are two drawbacks which make EA-based sensor ontology matchers incapable of effectively searching the optimal solution for sensor ontology matching problem. To improve the efficiency of EA-based sensor ontology matching technique, in this paper, a new optimal model of sensor ontology matching problem is first constructed, a novel sensor concept similarity measure is then presented to determine the identical sensor concepts, and finally, a problem-specific Compact Evolutionary Tabu Search algorithm (CETS) is presented to efficiently determine the sensor ontology alignment. In particular, CETS combines Compact Evolutionary Algorithm (global search) and Tabu Search algorithm (local search), and this marriage between global search and local search allows keeping high solution diversity via PV (reducing the possibility of the premature convergence) and increasing the convergence speed via the local search (reducing the runtime). The experimental results show that comparing with the state-of-the-art sensor ontology matching techniques, CETS can more efficiently determine the high-quality alignments.