Reviewerly/exHarmony huggingface dataset:
Numeral Attachment with Auxiliary Tasks

In this paper we propose the task of numeral attachment to detect the attached target of a numeral. Compared with other kinds of named entities, numerals provide richer and more crucial information in some domains. Fine-grained understanding of the information embedded in numerals is a fundamental challenge. We develop NumAttach, a pilot dataset for the proposed task based on tweets. Two main challenges of this task include the informal writing style in tweets and the representation of numerals. To address these challenges, we present an embedding technique that considers word and numeral information simultaneously. Furthermore, we design a joint learning model with the capsule network to accomplish the proposed task. We also release NumAttach to the research community as a resource.
A parallel vision approach to scene-specific pedestrian detection

In recent years, with the development of computing power and deep learning algorithms, pedestrian detection has made great progress. Nevertheless, once a detection model trained on generic datasets (such as PASCAL VOC and MS COCO) is applied to a specific scene, its precision is limited by the distribution gap between the generic data and the specific scene data. It is difficult to train the model for a specific scene, due to the lack of labeled data from that scene. Even though we manage to get some labeled data from a specific scene, the changing environmental conditions make the pre-trained model perform bad. In light of these issues, we propose a parallel vision approach to scene-specific pedestrian detection. Given an object detection model, it is trained via two sequential stages: (1) the model is pre-trained on augmented-reality data, to address the lack of scene-specific training data; (2) the pre-trained model is incrementally optimized with newly synthesized data as the specific scene evolves over time. On publicly available datasets, our approach leads to higher precision than the models trained on generic data. To tackle the dynamically changing scene, we further evaluate our approach on the webcam data collected from Church Street Market Place, and the results are also encouraging.
Visuo-tactile congruency influences the body schema during full body ownership illusion

Previous research showed that full body ownership illusions in virtual reality (VR) can be robustly induced by providing congruent visual stimulation, and that congruent tactile experiences provide a dispensable extension to an already established phenomenon. Here we show that visuo-tactile congruency indeed does not add to already high measures for body ownership on explicit measures, but does modulate movement behavior when walking in the laboratory. Specifically, participants who took ownership over a more corpulent virtual body with intact visuo-tactile congruency increased safety distances towards the laboratory's walls compared to participants who experienced the same illusion with deteriorated visuo-tactile congruency. This effect is in line with the body schema more readily adapting to a more corpulent body after receiving congruent tactile information. We conclude that the action-oriented, unconscious body schema relies more heavily on tactile information compared to more explicit aspects of body ownership.
Evaluating Resource-Lean Cross-Lingual Embedding Models in Unsupervised Retrieval

Cross-lingual embeddings (CLE) facilitate cross-lingual natural language processing and information retrieval. Recently, a wide variety of resource-lean projection-based models for inducing CLEs has been introduced, requiring limited or no bilingual supervision. Despite potential usefulness in downstream IR and NLP tasks, these CLE models have almost exclusively been evaluated on word translation tasks. In this work, we provide a comprehensive comparative evaluation of projection-based CLE models for both sentence-level and document-level cross-lingual Information Retrieval (CLIR). We show that in some settings resource-lean CLE-based CLIR models may outperform resource-intensive models using full-blown machine translation (MT). We hope our work serves as a guideline for choosing the right model for CLIR practitioners.
Ethical considerations in the use of GPS-based movement tracking in health research – lessons from a care-seeking study in rural west India

Use of technology for movement tracking for health research in low and middle income countries invokes ethical challenges especially those related to privacy, confidentiality and risk of spatial re-identification.
Sentiment Analysis on Online Product Reviews

Today, people are exchanging their thoughts through online Web forums, blogs, and different social media platforms. Sometimes, they are giving reviews and opinions on different products, brand, and their services. Their reviews toward a product not only improve the product quality but also influence purchase decisions of the consumers. Thus, product review analysis is a widely accepted platform where consumer can easily aware of their requirements. In this experiment, we track 568,454 fine food reviews of 74,258 products and 256,059 users on Amazon over a period of ten years. To analyze the result, we select six most popular products and users based on the plain text review, and NRC emotion lexicon is used which can be categorized eight basic emotions and two sentiments. Word cloud also help our research to make comparisons between the eight emotion categories. Our results show that how sentiment analysis will help to identify the consumers' behaviors and overcome those risks to meet the consumers' satisfaction.
Evaluating Visual Explanations for Similarity-Based Recommendations

Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance.
Deep scaled dot-product attention based domain adaptation model for biomedical question answering

Biomedical text mining is becoming increasingly important as the number of biomedical documents grow rapidly. Deep learning has boosted the development of biomedical text mining models. However, as deep learning models require a large amount of training data, a hierarchical attention based transfer learning model is proposed in this paper for the question answering task in biomedical field which lacks of sufficient training data. We adopt BERT (Bidirectional Encoder Representation Transformers), which has the ability to learn from large-scale unsupervised data, to enrich the semantic representation in our model. Especially, the scaled dot-product attention mechanism captures the question interaction clues for passage encoding. The domain adaptation technique of fine-tuning is used to reinforce the performance, which penalizes the deviations from the source model’s parameters and remembers the knowledge of source domain. We evaluate the system performance on the open data set of BioASQ-Task B. The results show that our system achieves the state-of-the-art performance without any handcrafted features and outperforms the best solution for factoid questions in 2016 and 2017 BioASQ-Task B.
A separation–aggregation network for image denoising

Image denoising is the problem that aims at recovering a clean image from a noisy counterpart. A promising solution for image denoising is to employ an appropriate deep neural network to learn a hierarchical mapping function from the noisy image to its clean counterpart. This mapping function, however, is generally difficult to learn since the potential feature space of the noisy patterns can be huge. To overcome this difficulty, we propose a separation–aggregation strategy to decompose the noisy image into multiple bands, each of which exhibits one kind of pattern. Then a deep mapping function is learned for each band and the mapping results are ultimately assembled to the clean image. By doing so, the network only needs to deal with the compositing components of the noisy image, thus makes it easier to learn an effective mapping function. Moreover, as any image can be viewed as a composition of some basic patterns, our strategy is expected to better generalize to unseen images. Inspired by this idea, we develop a separation–aggregation network. The proposed network consists of three blocks, namely a convolutional separation block that decomposes the input into multiple bands, a deep mapping block that learns the mapping function for each band, and a band aggregation block that assembles the mapping results. Experimental results demonstrate the superiority of our strategy over counterparts without image decomposition.
Edge-Based Live Video Analytics for Drones

Real-time video analytics on small autonomous drones poses several difficult challenges at the intersection of wireless bandwidth, processing capacity, energy consumption, result accuracy, and timeliness of results. In response to these challenges, this paper describes four strategies to build adaptive computer vision pipelines for domains such as search-and-rescue, surveillance, and wildlife conservation. Our experimental results show that a judicious combination of drone-based processing and edge-based processing can save substantial wireless bandwidth and thus improve scalability, without compromising result accuracy or latency.
Promoting semantic diversity in multi-objective genetic programming

The study of semantics in Genetic Programming (GP) has increased dramatically over the last years due to the fact that researchers tend to report a performance increase in GP when semantic diversity is promoted. However, the adoption of semantics in Evolutionary Multi-objective Optimisation (EMO), at large, and in Multi-objective GP (MOGP), in particular, has been very limited and this paper intends to fill this challenging research area. We propose a mechanism wherein a semantic-based distance is used instead of the widely known crowding distance and is also used as an objective to be optimised. To this end, we use two well-known EMO algorithms: NSGA-II and SPEA2. Results on highly unbalanced binary classification tasks indicate that the proposed approach produces more and better results than the rest of the three other approaches used in this work, including the canonical aforementioned EMO algorithms.
Quantum image encryption based on phase-shift transform and quantum Haar wavelet packet transform

An algorithm of quantum image encryption based on a normal arbitrary superposition state (NASS) is proposed by using quantum geometric transform, phase-shift transform, and quantum Haar wavelet packet transform (QHWPT) in this paper. Firstly, to scramble an image, we design an implementation circuit of quantum image pixel exchanges along diagonal lines. Next, [Formula: see text] keys randomly distributed between 0 and [Formula: see text] are encoded in the encryption algorithm by using phase-shift transform. The huge key space makes it difficult to attack encrypted images. Then, we store image information in low frequency using QHWPT to improve encryption effect. After that, the encryption algorithms of gray image and color image are simulated by using MATLAB, respectively. Meanwhile, evaluation indexes such as histogram, complexity and correlation of adjacent pixels are analyzed. Simulation experiments show that the proposed algorithm is valid.
A bird’s eye view of my village – Developing participatory geospatial methodology for local level land use planning in the Southern Highlands of Tanzania

Despite the large number of participatory mapping and participatory geographical information system (PGIS) applications developed since the 1990s, few studies have utilized participatory mapping in formal planning processes. Evidence is needed regarding their practical applicability in rural land use planning in the Global South and their effectiveness in decision-making in formal planning processes. In this paper, we present participatory mapping and planning methodology that we have co-developed for official village land use planning processes in Tanzania and assess the method's influence on spatial data quality as well as deliberation and spatial understanding and learning among the participants. We describe 11 literature-based criteria for integrating participatory mapping into spatial planning processes and use them in our assessment. The assessment includes analysis of village land use plan (VLUP) maps and observations as well as interviews and group discussions with participants and facilitators of the planning process. We show that the participatory mapping method with georeferenced images is a powerful tool to capture local spatial knowledge from a wide range of stakeholders and increase the quality of and confidence in spatial planning. As a visual aid, the georeferenced image supports deliberation and detailed examination of the landscape, enhancing spatial understanding and learning about the village landscape. Apart from generating local spatial data, we show that the participatory geospatial method supports the decision-making capacity of participants, which is important for the effectiveness of the method in formal land use planning processes.
Online Topology Inference from Streaming Stationary Graph Signals

We address the problem of online topology inference from streaming nodal observations of graph signals generated by linear diffusion dynamics on the sought graph. To that end, we leverage the stationarity of the signals and use the so-called graph-shift operator (GSO) as a matrix representation of the graph. Under this model, estimated covariance eigenvectors obtained from streaming independent graph signals diffused on the sought network are a valid estimator of the GSO's spectral templates. We develop an ADMM algorithm to find a sparse and structurally admissible GSO given the eigenvectors estimate. Then, we propose an online scheme that upon sensing new diffused observations, efficiently updates eigenvectors (thus makes more accurate on expectation) and performs only one or a few iterations of the mentioned ADMM until the new data is observed. Numerical tests illustrate the effectiveness of the proposed topology inference approach in recovering large scale graphs, adapting to streaming information, and accommodating changes in the sought network.
Snakes in Paradise?: Insecure Python-Related Coding Practices in Stack Overflow

Despite being the most popular question and answer website for software developers, answers posted on Stack Overflow (SO) are susceptible to contain Python-related insecure coding practices. A systematic analysis on how frequently insecure coding practices appear in SO answers can help the SO community assess the prevalence of insecure Python code blocks in SO. An insecure coding practice is recurrent use of insecure coding patterns in Python. We conduct an empirical study using 529,054 code blocks collected from Python-related 44,966 answers posted on SO. We observe 7.1% of the 44,966 Python-related answers to include at least one insecure coding practice. The most frequently occurring insecure coding practice is code injection. We observe 9.8% of the 7,444 accepted answers to include at least one insecure code block. We also find user reputation not to relate with the presence of insecure code blocks, suggesting that both high and low-reputed users are likely to introduce insecure code blocks.
Local Detection of Stereo Occlusion Boundaries

Stereo occlusion boundaries are one-dimensional structures in the visual field that separate foreground regions of a scene that are visible to both eyes (binocular regions) from background regions of a scene that are visible to only one eye (monocular regions). Stereo occlusion boundaries often coincide with object boundaries, and localizing them is useful for tasks like grasping, manipulation, and navigation. This paper describes the local signatures for stereo occlusion boundaries that exist in a stereo cost volume, and it introduces a local detector for them based on a simple feedforward network with relatively small receptive fields. The local detector produces better boundaries than many other stereo methods, even without incorporating explicit stereo matching, top-down contextual cues, or single-image boundary cues based on texture and intensity.
Investigating the Impact of Multiple Dependency Structures on Software Defects

Over the past decades, numerous approaches were proposed to help practitioner to predict or locate defective files. These techniques often use syntactic dependency, history co-change relation, or semantic similarity. The problem is that, it remains unclear whether these different dependency relations will present similar accuracy in terms of defect prediction and localization. In this paper, we present our systematic investigation of this question from the perspective of software architecture. Considering files involved in each dependency type as an individual design space, we model such a design space using one DRSpace. We derived 3 DRSpaces for each of the 117 Apache open source projects, with 643,079 revision commits and 101,364 bug reports in total, and calculated their interactions with defective files. The experiment results are surprising: the three dependency types present significantly different architectural views, and their interactions with defective files are also drastically different. Intuitively, they play completely different roles when used for defect prediction/localization. The good news is that the combination of these structures has the potential to improve the accuracy of defect prediction/localization. In summary, our work provides a new perspective regarding to which type(s) of relations should be used for the task of defect prediction/localization. These quantitative and qualitative results also advance our knowledge of the relationship between software quality and architectural views formed using different dependency types.
Hybrid chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots

Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In these assignments, one important issue is to obtain appropriate paths for robots especially in some complex applications. In this context, a novel hybrid swarm intelligence algorithm, ant colony optimization enhanced by chaos-based particle swarm optimization, is proposed in this article to deal with the path planning problem for landfill inspection robots in Asahikawa, Japan. In chaos-based particle swarm optimization, Chebyshev chaotic sequence is used to generate the random factors for particle swarm optimization updating formula so as to effectively adjust particle swarm optimization parameters. This improved model is applied to optimize and determine the hyper parameters for ant colony optimization. In addition, an improved pheromone updating strategy which combines the global asynchronous feature and “Elitist Strategy” is employed in ant colony optimization in order to use global information more appropriately. Therefore, the iteration number of ant colony optimization invoked by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively. Comparative simulation experiments show that the chaos-based particle swarm optimization-ant colony optimization has a rapid search speed and can obtain solutions with similar qualities.
Is QUIC becoming the New TCP? On the Potential Impact of a New Protocol on Networked Multimedia QoE

Over the last years, QUIC (Quick UDP Internet Connections) has become the default protocol for networked communication of Google services, heralded as improved successor of the prevailing Transport Control Protocol (TCP). While the deployment of QUIC is increasing, QUIC is also planned to be the foundation of HTTP/3, the next generation of the HTTP protocols, which drive almost all applications on the Web. Given these developments, this paper aims to raise the awareness of the QoE research community to the increasing presence of QUIC, which likely brings implications for QoE monitoring and management of networked multimedia applications, as well as for the overall QoE research agenda. In particular, a major promise during the introduction of QUIC has been the improvement of the QoE of web-based applications (like browsing and video) by overcoming certain limitations and inefficiencies of TCP. In order to validate this claim, a measurement study was conducted to test whether the promised QoE benefits of QUIC are indeed noticeable for end users of streaming and browsing services. Surprisingly, no evidence for any QoE improvement of QUIC over TCP could be found. This way this paper aims to demonstrate how QoE research can and should successfully address relevant current and future developments on the Internet.
Mobile QoE prediction in the field

Quality of experience (QoE) models quantify the relationship between user experience and network quality of service. With the exception of a few studies, most research on QoE has been conducted in laboratory conditions. Therefore, in order to validate and develop QoE models for the wild, researchers should carry out large scale field studies. This paper contributes data and observations from such a large-scale field study on mobile devices carried out in Finland with 292 users and 64,036 experience ratings. 74% of the ratings are associated with Wifi or LTE networks. We report descriptive statistics and classification results predicting normal vs. bad QoE in in-the-wild measurements. Our results illustrate a 20% improvement over baselines for standard classification metrics (G-Mean). Furthermore, both network features (such as delay) and non-network features (such as device memory) show importance in the models. The models’ performance suggests that mobile QoE prediction remains a difficult problem in field conditions. Our results help inform future modeling efforts and provide a baseline for such real-world mobile QoE prediction.