Reviewerly/exHarmony huggingface dataset:
Planning an experiment in a virtual environment reality as a place of research on human behaviour using methods of neuroscience measurement – bibliometric analysis and methodological approach

The methods used in cognitive neuroscience allow through measurement methods to analyze the behavior of subjects. In the opinion of the authors, the application of the experiment in a virtual environment and the measurement of brain activity can obtain a detailed analysis of the information and knowledge of the subject. For this to happen, however, it is important that the entire course of the study is carried out in accordance with the assumptions, without errors that could affect the incorrect interpretation of the results obtained. Planning an experiment focuses on planning all the details involved in carrying out the study (Hicks, 1973, Goodwin, 2008). Therefore, in the course of the research, the authors sought information on the methodology of experimental research carried out in a virtual reality environment with the use of selected tools of cognitive neuroscience, including in particular the use of virtual reality (VR), electroencephalograph (EEG) and eye tracking. Valuable knowledge and information on this topic can be found, among others, in the publication of B Rey, M Alcañiz in the article "Research in neuroscience and virtual reality" [1] in which the importance of the implementation of research in virtual reality is highlighted. The authors believe that VR is an ideal tool to "generate controlled environments the can be used to observe human behavior. Different aspects can be analyzed from a neuroscience perspective, including perception, control of movement, learning, memory, and emotional aspects. " However, in order to assess the extent to which the experimental method in combination with technology allows for reliable information and knowledge management, it was necessary to look at considerations of the research methodology. Therefore, as the first stage, a bibliometric study was adopted, the analysis of the content contained in three databases such as WoS, Scopus, and Google Scholar. The analyses carried out showed a small percentage of publications presenting the methodology of implementation of an experimental study in VR treated as an environment in which research is carried out, within which the aforementioned methods of measurement from the field of neuroscience were applied. Based on the analysis, the authors, on the basis of selected materials in which the research processes have been well-grounded in methodology, as well as on their own research experience, will present the stages of design and implementation of an experimental study carried out in the virtual reality environment using the indicated methods of cognitive neuroscience. Based on a case study, they presented the activities carried out in the course of analyzed events, as well as pointed to potential errors that may occur in the course of research. A supermarket was taken as the virtual location of the study.
Open Domain Question Answering over Knowledge Graphs Using Keyword Search, Answer Type Prediction, SPARQL and Pre-trained Neural Models

Question Answering (QA) in vague or complex open domain information needs is hard to be adequate, satisfying and pleasing for end users. In this paper we investigate an approach where QA complements a general purpose interactive keyword search system over RDF. We describe the role of QA in that context, and we detail and evaluate a pipeline for QA that involves a general purpose entity search service over RDF, answer type prediction, entity enrichment through SPARQL, and pre-trained neural models. The fact that we start from a general purpose keyword search over RDF, makes the proposed pipeline widely applicable and realistic, in the sense that it does not pre-suppose the availability of knowledge graph-specific training dataset. We evaluate various aspects of the pipeline, including the effect of answer type prediction, as well as the performance of QA over existing benchmarks. The results show that, even by using different data sources for training, the proposed pipeline achieves a satisfactory performance. Moreover we show that the ranking of entities for QA can improve the entity ranking.
Face Detection and Recognition System

Facial feature detection and recognition are widely used in current world scenarios and technologies. Almost every smartphone is embedding facial recognition & detection in security matters like phone unlocking. Another increasing use of this type of technology is being noticed in cameras and social media like snap chat. In this paper, we developed a system/platform in which facial features containing noise, eyes, mouth, and eyebrows can be detected from an image. The developed system can also recognize a human subject from two different input images. The proposed system works in two steps-in the first step, image segmentation has been employed on more than a few images to enhance the images by using various edge detection techniques and filtering out the noise. In the second part, features are extracted from an idea through histogram of gradient (HOG). The entire facial feature detection and recognition system was developed with a histogram of oriented gradients (HOG), support vector machine (SVM), and K nearest neighbor (KNN). The proposed system gives an accuracy of 96% in the detection and recognition of facial features and human subjects. The projected strategy of procedures offers improved truth once appeared otherwise in relevance the others mixture of ways in which. The assessment has been done in the Python platform on authentic images of the Indian face database.
Semantic Snapping for Guided Multi-View Visualization Design

Visual information displays are typically composed of multiple visualizations that are used to facilitate an understanding of the underlying data. A common example are dashboards, which are frequently used in domains such as finance, process monitoring and business intelligence. However, users may not be aware of existing guidelines and lack expert design knowledge when composing such multi-view visualizations. In this paper, we present semantic snapping, an approach to help non-expert users design effective multi-view visualizations from sets of pre-existing views. When a particular view is placed on a canvas, it is "aligned" with the remaining views-not with respect to its geometric layout, but based on aspects of the visual encoding itself, such as how data dimensions are mapped to channels. Our method uses an on-the-fly procedure to detect and suggest resolutions for conflicting, misleading, or ambiguous designs, as well as to provide suggestions for alternative presentations. With this approach, users can be guided to avoid common pitfalls encountered when composing visualizations. Our provided examples and case studies demonstrate the usefulness and validity of our approach.
Inferring Hidden IoT Devices and User Interactions via Spatial-Temporal Traffic Fingerprinting

With the popularization of Internet of Things (IoT) devices in smart home and industry fields, a huge number of IoT devices are connected to the Internet. However, what devices are connected to a network may not be known by the Internet Service Provider (ISP), since many IoT devices are placed within small networks (e.g., home networks) and are hidden behind network address translation (NAT). Without pinpointing IoT devices in a network, it is unlikely for the ISP to appropriately configure security policies and effectively manage the network. Additionally, inferring fine-grained user interactions of IoT devices is also an interesting yet unresolved problem. In this paper, we design an efficient and scalable system via spatial-temporal traffic fingerprinting from an ISP’s perspective in consideration of practical issues like learning-testing asymmetry. Our system can accurately identify typical IoT devices in a network, with the additional capability of identifying what devices are hidden behind NAT and the number of each type of device that share the same IP address. Our system can also detect user interactions and meanwhile identify their (concurrent) number through a multi-output regression model. Through extensive evaluation, we demonstrate that the system can generally identify IoT devices with an F1-Score above 0.999, and estimate the number of the same type of IoT device behind NAT with an average error below 5%. By studying 29 user interactions of 7 devices, we show that our system is promising in detecting user interactions.
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

Deep neural networks for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision-making. Recent findings have shown that existing medical AEs are easy to detect in feature space. To better understand this phenomenon, we thoroughly investigate the characteristic of traditional medical AEs in feature space. Specifically, we first perform a stress test to reveal the vulnerability of medical images and compare them to natural images. Then, we theoretically prove that the existing adversarial attacks manipulate the prediction by continuously optimizing the vulnerable representations in a fixed direction, leading to outlier representations in feature space. Interestingly, we find this vulnerability is a double-edged sword that can be exploited to help hide AEs in the feature space. We propose a novel hierarchical feature constraint (HFC) as an add-on to existing white-box attacks, which encourages hiding the adversarial representation in the normal feature distribution. We evaluate the proposed method on two public medical image datasets, namely Fundoscopy and Chest X-Ray. Experimental results demonstrate the superiority of our HFC as it bypasses an array of state-of-the-art adversarial medical AEs detector more efficiently than competing adaptive attacks. Our code is available at https://github.com/qsyao/Hierarchical_Feature_Constraint.
Efficient Single-Shot Multi-Object Tracking for Vehicles in Traffic Scenarios

Multi-object tracking is a significant field in computer vision since it provides essential information for video surveillance and analysis. Several different deep learning-based approaches have been developed to improve the performance of multi-object tracking by applying the most accurate and efficient combinations of object detection models and appearance embedding extraction models. However, two-stage methods show a low inference speed since the embedding extraction can only be performed at the end of the object detection. To alleviate this problem, single-shot methods, which simultaneously perform object detection and embedding extraction, have been developed and have drastically improved the inference speed. However, there is a trade-off between accuracy and efficiency. Therefore, this study proposes an enhanced single-shot multi-object tracking system that displays improved accuracy while maintaining a high inference speed. With a strong feature extraction and fusion, the object detection of our model achieves an AP score of 69.93% on the UA-DETRAC dataset and outperforms previous state-of-the-art methods, such as FairMOT and JDE. Based on the improved object detection performance, our multi-object tracking system achieves a MOTA score of 68.5% and a PR-MOTA score of 24.5% on the same dataset, also surpassing the previous state-of-the-art trackers.
User behavior prediction via heterogeneous information in social networks

With the development of online social networks, user behavior prediction based on the data collected from these social networks has attracted increasing attention. In heterogeneous social networks, a node usually has several heterogeneous attributes to describe itself from different angles. However, most existing methods only utilize an attribute of each node and neglect other heterogeneous attributes. Therefore, this paper proposes a new user heterogeneous information embedding method, called user heterogeneous information embedding (UHIE). This method utilizes the attention mechanism to aggregate the heterogeneous attribute information of each neighbor to obtain their low-dimension representation. Then, the graph neural network is employed to aggregate the multi-relational information from neighbors to obtain the low-dimension representation of nodes. Furthermore, a new soft thresholding method is proposed to eliminate the unimportant information, called multi-head self-attention soft thresholding (MSST), which employs the multi-head self-attention mechanism to calculate an importance threshold for each feature. Based on UHIE and MSST, a new user behavior prediction model is proposed, called Heterogeneous Residual Self-Attention Shrinkage Network (HRSN). This model utilizes UHIE to aggregate heterogeneous information including all heterogeneous attribute information of nodes, and employs MSST to eliminate unimportant information. The experimental results on three real-world datasets show the superiority of the proposed model.
Using Machine Learning to Detect Events on the Basis of Bengali and Banglish Facebook Posts

In modern times, ensuring social security has become the prime concern for security administrators. The widespread and recurrent use of social media sites is creating a huge risk for the lives of the general people, as these sites are frequently becoming potential sources of the organization of various types of immoral events. For protecting society from these dangers, a prior detection system which can effectively detect events by analyzing these social media data is essential. However, automating the process of event detection has been difficult, as existing processes must account for diverse writing styles, languages, dialects, post lengths, and et cetera. To overcome these difficulties, we developed an effective model for detecting events, which, for our purposes, were classified as either protesting, celebrating, religious, or neutral, using Bengali and Banglish Facebook posts. At first, the collected posts’ text were processed for language detection, and then, detected posts were pre-processed using stopwords removal and tokenization. Features were then extracted from these pre-processed texts using three sub-processes: filtering, phrase matching of specific events, and sentiment analysis. The collected features were ultimately used to train our Bernoulli Naive Bayes classification model, which was capable of detecting events with 90.41% accuracy (for Bengali-language posts) and 70% (for the Banglish-form posts). For evaluating the effectiveness of our proposed model more precisely, we compared it with two other classifiers: Support Vector Machine and Decision Tree.
A Survey on Object Detection and Tracking in a Video Sequence

Object detection and tracking are crucial and strenuous tasks in many computer vision applications. The algorithms for detecting and tracking objects in videos are manifold. This paper discusses different stages in object tracking. Also, it reviews various approaches available for detection, classification and tracking of objects in a video. Merits and demerits of the existing methods are depicted in this paper.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study

Abstract Recent advancements in open-domain question answering (ODQA), that is, finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets. However, progress in QA over book stories (Book QA) lags despite its similar task formulation to ODQA. This work provides a comprehensive and quantitative analysis about the difficulty of Book QA: (1) We benchmark the research on the NarrativeQA dataset with extensive experiments with cutting-edge ODQA techniques. This quantifies the challenges Book QA poses, as well as advances the published state-of-the-art with a ∼7% absolute improvement on ROUGE-L. (2) We further analyze the detailed challenges in Book QA through human studies.1 Our findings indicate that the event-centric questions dominate this task, which exemplifies the inability of existing QA models to handle event-oriented scenarios.
CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this paper, we proposed an IR-based model CodeMatcher that inherits the advantages of DeepCS, while it can leverage the indexing technique in the IR-based model to accelerate the search response time substantially. CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool, and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query. We verified its effectiveness on a large-scale codebase with ~41k repositories. Experimental results showed that CodeMatcher achieves an MRR of 0.60, outperforming DeepCS, CodeHow, and UNIF by 82%, 62%, and 46% respectively. Our proposed model is over 1.2k times faster than DeepCS. Moreover, CodeMatcher outperforms GitHub and Google search by 46% and 33% respectively in terms of MRR. We also observed that: fusing the advantages of IR-based and DL-based models is promising; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code.
Extreme-Quality Computational Imaging via Degradation Framework

To meet the space limitation of optical elements, free-form surfaces or high-order aspherical lenses are adopted in mobile cameras to compress volume. However, the application of free-form surfaces also introduces the problem of image quality mutation. Existing model-based deconvolution methods are inefficient in dealing with the degradation that shows a wide range of spatial variants over regions. And the deep learning techniques in low-level and physics-based vision suffer from a lack of accurate data. To address this issue, we develop a degradation framework to estimate the spatially variant point spread functions (PSFs) of mobile cameras. When input extreme-quality digital images, the proposed framework generates degraded images sharing a common domain with real-world photographs. Supplied with the synthetic image pairs, we design a Field-Of-View shared kernel prediction network (FOV-KPN) to perform spatial-adaptive reconstruction on real degraded photos. Extensive experiments demonstrate that the proposed approach achieves extreme-quality computational imaging and outperforms the state-of-the-art methods. Furthermore, we illustrate that our technique can be integrated into existing postprocessing systems, resulting in significantly improved visual quality.
Opaque Obstacles: The Role of Stigma, Rumor, and Superstition in Limiting Women’s Access to Computing in Rural Bangladesh

Marginalized communities’ access to and use of ICT have long been a concern in HCI4D and social computing. Many works in this domain have pointed out that the challenges to access to ICT often go beyond limited computing resources and skills and frequently include many other socio-cultural factors. In this paper, we report three of the factors that arose while studying rural Bangladeshi women’s access to ICT: stigma, rumor, and superstition. Through an eight-month-long mix-method study with 23 rural women in Jessore, we explored the forms of fear and resistance to use computing devices prevalent among this population, particularly among the women we studied. We report how their stigma, rumors and superstitions often entangled with each other and created a gender-specific resistance to women’s ICT use. This paper further discusses how this resistance was connected to a weak economy and insufficient legal and educational infrastructure in the rural community. We extend the discussion to implications for design, policy, patriarchy, and other social practices to address these human factors in HCI4D and social sustainability scholarship.
TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes

The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end, we introduce TransforMesh, a spatio-temporal network based on transformers that models longitudinal shape changes on 3D anatomical meshes. While transformer and mesh networks have recently shown impressive performances in natural language processing and computer vision, their application to medical image analysis has been very limited. To the best of our knowledge, this is the first work that combines transformer and mesh networks. Our results show that TransforMesh can model shape trajectories better than other baseline architectures that do not capture temporal dependencies. Moreover, we also explore the capabilities of TransforMesh in detecting structural anomalies of the hippocampus in patients developing AD.
Adaptive operator selection with reinforcement learning

Operator selection plays a crucial role in the efficiency of heuristic-based problem solving algorithms, especially, when a pool of operators is used to let algorithms dynamically select operators to produce new candidate solutions. A sequence of selected operators forms up throughout the search which impacts the success of the algorithms. Successive operators in a bespoke sequence can be complementary and therefore diversify the search while randomly selected operators are not expected to behave in this way. State of art adaptive selection schemes have been proposed to select the best next operator without considering the problem state in the process. In this study, a reinforcement learning algorithm is proposed to embed in a standard artificial bee colony algorithm for taking the problem state on board in operator selection process. The proposed approach implies mapping the problem states to the best fitting operators in the pool so as to achieve higher diversity and shape up an optimum operator sequence throughout the search process. The experimental study successfully demonstrates that the proposed idea works towards higher efficiency. The state of art approaches are outperformed with respect to the quality of solution in solving Set Union Knapsack problem over 30 benchmarking instances.