Reviewerly/exHarmony huggingface dataset:
FROM WATCHED AT WORK TO WATCHED AT HOME: WORKPLACE SURVEILLANCE DURING A PANDEMIC

The COVID-19 pandemic has created new opportunities and new tensions related to workplace surveillance. Monitoring workers via digital tools to analyze everything from keystrokes to email and social media to the websites they visit is increasingly common, and the shift to remote work in the early days of the pandemic led many employers to consider new ways to monitor their employees while working from home. In this paper, we consider how the pandemic has affected office workers’ experience of surveillance, focusing on the types of monitoring they currently experience and their concerns related to future forms of surveillance. In particular, we unpack the sociotechnical implications of shifting work surveillance practices due to COVID-19, focusing on how evolving and emergent workplace surveillance practices may impact workers. Using factorial vignettes, survey respondents (N=645) read and responded to 35 scenarios about future workplace surveillance practices. Each scenario randomly varied four factors about workplace monitoring: the type of data being collected, the purpose for data collection, the actors who can access the data, and the transmission principle guiding data collection. For each scenario, respondents assessed both the appropriateness of each scenario and how concerning they found it. We evaluate this data, as well as data about respondents’ work environment before and during the pandemic, using Nissenbaum’s framework of privacy as contextual integrity. We also consider the potential harms associated with different types of monitoring.
Poisson Regressions: A Little Fishy

When analyzing count data (such as number of questions answered correctly), psychologists often use Poisson regressions. We show through simulations that violating the assumptions of a Poisson distribution even slightly can lead to false positive rates more than doubling, and illustrate this issue with a study that finds a clearly spurious but highly significant connection between seeing the color blue and eating fish candies. In additional simulations we test alternative methods for analyzing count-data and show that these generally do not suffer from the same inflated false positive rate, nor do they result in much higher false negatives in situations where Poisson would be appropriate.
Avoid Overfitting in Deep Reinforcement Learning: Increasing Robustness Through Decentralized Control

Deep Reinforcement Learning is known to be brittle towards selection of appropriate hyperparameters. In particular, the selection of the structure of employed Deep Neural Networks has shown to be important and overfitting has become a common problem for DRL approaches. This study, first, analyzes how severe overfitting in DRL is in standard continuous control problems. Secondly, we argue that this might be partially due to the centralized perspective in control in which a single holistic controller is used that has to learn from all possible sensory inputs. As this is usually a high dimensional space, it appears natural that a Neural Network controller of high capacity starts to pick up non-meaningful correlations when relying on limited data during training. As a consequence, large Neural Network controller start to base their decisions on unimportant inputs. As a contrast, we offer a decentralized perspective in which control is distributed to local modules that act on local sensory inputs of much lower dimensionality. Such a prior of local inputs is biological inspired and shows, on the one hand, much faster learning, and, on the other hand, it is more robust against overfitting. Last, as decentralization impacts input (and output) dimensionality, we evaluated different common Neural Network initialization schemes and found Glorot initialization providing the most robust results.
Real-Time Analog Event-Detection for Event-Based Synchronous Sampling of Sparse Sensor Signals

The unprecedented rise of the internet-of-things (IoT) has led to an enormous rate of data generation from sensors and IoT devices, calling for an urgent need for more intelligent approaches of data acquisition from such sensors. This paper proposes an event-based sampling technique for selective acquisition of event-data from sparse sensor signals, produced by remote sensors and IoT devices. The proposed technique simultaneously combines advantageous features of uniform (synchronous) and non-uniform (asynchronous) techniques. In the proposed approach, event-detection is achieved in real-time in the analog domain, prior to analog-to-digital conversion (ADC) and digital processing, by means of an analog event-detection (AED) circuit. A proof-of-concept design for the AED circuit is implemented and analyzed through experiments and extensive SPICE simulations, demonstrating its capability of detecting the onset of an event in real-time; with speeds on the order of microseconds. Such rapid analog event-detection can enable the real-time control of the sampling process, such that no sampling would occur unless there is an event, giving rise to a richer information content in the acquired data. Moreover, the proposed technique allows all system blocks to remain in sleep-mode until an event is detected, dramatically reducing their overall power consumption. The impact of the proposed technique is further demonstrated on an important industrial application; seismic data acquisition, by testing the design - through SPICE simulation – on real seismic data obtained from seismic surveys for oil and gas exploration.
A probabilistic approximate logic for neuro-symbolic learning and reasoning

• Probabilistic semantic framework integrating logic and machine learning. • Systematic approach to incorporate domain knowledge (theories/hypotheses) into learning. • Wide range of learning settings and workflows supported (from supervised to unsupervised). • Leverages Maude (symbolic engine) and TensorFlow/Keras (deep learning framework). • Foundation to explore synergies of neuro-symbolic architectures (based on imagination). As witnessed by recent advances in deep learning technologies, neural network models of very high complexity have been successfully applied in many data-rich domains. Challenges remain, however, if the amount of training data is severely limited, which is often the case due to the cost of acquiring such data or due to interest in systems that are constantly evolving thereby imposing natural limits on how much data can be collected. The core hypothesis explored in this paper is that data (to some degree) can be substituted by domain knowledge, not only addressing the limited data problem but also offering potential improvements in data-rich settings. For the representation of suitable domain theories, we propose Probabilistic Approximate Logic (PALO) to deal with the natural uncertainty associated with such representations and also to serve as a foundation for a new class of neuro-symbolic architectures, in which both neural and symbolic computations can be peacefully and synergistically integrated. Utilizing TensorFlow and Maude as neural and symbolic frameworks, respectively, we discuss our prototypical implementation of PALO in what we call the Logical Imagination Engine (LIME). By means of a small toy example, we convey a glimpse of its capabilities, but we also briefly discuss some real-world applications and how it may serve as a prototypical framework to explore a broader range of neuro-symbolic strategies in the future.
Eye Gaze Tracking and Blinking Detection

Eye gaze tracking is a technique used for measuring the eye gaze point of human eyes and their degree of freedom with respect to the head pose using various hardware. There are many applications which are based on the aspects of the movements of eyes and are mainly used in driver’s safety, virtual reality, and face detection and particularly in human–computer interaction. In this paper, we propose an eye gaze detection system that can classify between three different gaze directions (left, right, and center) along with the blinking detection using the Web camera in real time. In this process, the first step is to detect face from the real-time video input which is done with the help of Dlib in order to localize the eye region. After that, the proposed image processing algorithms are used to classify the gaze direction and for blinking detection. The results demonstrate the successful detection of blinking and classification of eye gaze direction under various conditions.
Student Aesthetic Education Recommendation App Based on Interest

This paper focuses on the construction of interest model, and comprehensive consideration of users' browsing, evaluation and other behaviors, to propose a learning system based on knowledge points. It analyzes the characteristics of learners' cognitive level and initializes the student model conforming to the learners' personal level. Then according to the analysis of various personalized recommendation algorithms, the user based recommendation algorithm, which is widely used in e-commerce collaborative filtering algorithm, is mainly used to design personalized learning resource recommendation APP platform for aesthetic education teaching. Starting from the business requirements of the teaching platform, the overall planning scheme of the platform is provided, and the detailed implementation strategy of the key recommendation sub module function is also performed. Finally, through the database design, interface design and function test analysis of APP mobile teaching platform, the feasibility and effectiveness of the system are proved.
Multi-Scale Spatial Transformer Network for LiDAR-Camera 3D Object Detection

Accurate 3D object detection has recently aroused interest in the context of emerging autonomous driving technologies. Existing approaches predominantly use LiDAR-Camera fusion method to fulfill this challenging task, while neglecting the fact that LiDAR and camera data are spatially correlated, and cannot well retain the edge information. To solve these problems, in this paper, we propose a novel LiDAR-Camera 3D object detection method, namely the Multi-scale Spatial Transformer Network (MST-Net). The proposed method exploits an innovative spatial alignment scheme based on the projection transformer network (PTN) to mitigate the effects of the perspective view caused by sensors. In the process of generating 3D bounding boxes, the Atrous Spatial Pyramid Pool (ASPP) is applied to spatially aligned fusion features in order to preserve edge information to the greatest extent. Extensive experiments are conducted on the popular dataset KITTI, and the results can demonstrate the superiority of the proposed method. In addition, the effectiveness of these two strategies has been illustrated in ablation studies.
Absentia: Secure Multiparty Computation on Ethereum

This paper describes a blockchain-based approach for secure function evaluation (SFE) in the setting where multiple participants have private inputs (multiparty computation) that no other individual should learn. The emphasis of Absentia is reducing the participants’ work to a bare minimum, where they can effectively have the computation performed in their absence and they can trust the result. While we use an SFE protocol (Mix and Match) that can operate perfectly well without a blockchain, the blockchain does add value in at least three important ways: (1) the SFE protocol requires a secure bulletin board and blockchains are the most widely deployed data structure with bulletin board properties (immutability and non-equivocation under reasonable assumptions); (2) blockchains provide a built-in mechanism to financially compensate participants for the work they perform; and (3) a publicly verifiable SFE protocol can be checked by the blockchain network itself, absolving the users of having to verify that the function was executed correctly. We benchmark Absentia on Ethereum. While it is too costly to be practical (a single gate costs thousands of dollars), it sets a research agenda for future improvements. We also alleviate the cost by composing it with Arbitrum, a layer 2 ‘roll-up’ for Ethereum which reduces the costs by 94%.
Four vignettes on interactions with Martin Seah: The impact of his work

Four brief vignettes briefly describe ways that Dr. Martin Seah influenced my career and those of many others. These brief stories highlight aspects of his (i) career‐long contributions to the quantification and accuracy of surface analysis, (ii) ability to identify and willingness to fill information gaps and needs, (iii) significant efforts to communicate the nature of the challenges and what was known about best practices in an engaging and often humorous manner, and (iv) many delightful professional and social interactions that also contributed to community impact.
An Organ Donation Management System (ODMS) based on Blockchain Technology for Tracking and Security Purposes

Blockchain is a viable technology for assisting in the deployment of electronic medical records, particularly in terms of data integration and data security. Recent scholarly studies on the use of blockchain for medical record data management have demonstrated that using this technology provides patients with complete control of their medical records. Data must be consistent and correct throughout the data transmission process to be efficient and clear. Decentralized data storage is the most appropriate strategy for storing data to expand storage functionality. The organ management system currently employs a client-server architecture. Because this type of technology stores data on a server, the data is lost and difficult to restore in the event of a server failure. This study proposes a blockchain-based organ donation management system (ODMS).
Reinforcement Syntactic Dependency Tree Reasoning for Target-Oriented Opinion Word Extraction

Target-oriented Opinion Word Extraction (TOWE) is a subtask of Aspect Based Sentiment Analysis (ABSA), which aims to extract fine-grained opinion terms for a given aspect term from a sentence. In TOWE task, syntactic dependency tree is useful as it provides explanation to identify opinion terms to the given aspect term. It is necessary to mine relationships between aspect and opinion terms for a better performance. Previous works introduced syntactic dependency tree into TOWE task but lacked of explicit explanation. In this paper, we propose a novel model named MM-TOWE, which leverages Monte-Carlo tree search to enhance Markov decision process (MDP) model for Target-oriented Opinion Word Extraction task. We formulate TOWE task as an MDP of reasoning over the syntactic dependency tree. By learning the dependency relationships between aspect terms and opinion terms, our model can reason a path for an explicit explanation. Extensive experimental results illustrate that our proposed model outperforms the state-of-the-art methods.
Linguistic Uncertainty in Clinical NLP: A Taxonomy, Dataset and Approach

Linguistic uncertainty is prevalent in electronic health records (EHRs). The ability to handle and preserve uncertainty in natural language is an essential skill for clinicians, facilitating decidability and effective clinical reasoning processes despite incomplete knowledge in some situations. This has been addressed by previous research in clinical NLP by the development of algorithms that detect uncertainty expressions. However, existing rule-based algorithms have limited uncertainty detection capabilities. Therefore, we seek to reformulate uncertainty detection as a supervised machine learning problem by (i) reevaluating the concept of uncertainty, (ii) embedding this understanding in an improved linguistic uncertainty taxonomy and (iii) introducing a new dataset of EHRs annotated for nine types of uncertainty – the first publicly available dataset of its kind. Many of our classes are novel and emphasise implicit uncertainties – a form of uncertainty that is ignored by existing algorithms, yet has crucial functions in clinical settings. Through an evaluation of our dataset, we demonstrate the scalability of our approach and its utility in relation to research on clinical information extraction.
Group-Based Deep Transfer Learning with Mixed Gate Control for Cross- Domain Recommendation

For modern recommender systems, the issue of data sparsity still often arises in many cases. To address this challenge, Cross-Domain Recommendation (CDR) has been explored by transferring knowledge learned from the source domain to alleviate the data sparsity in the target domain, and existing methods typically assume intra-domain samples share more commonalities than variabilities. However, it can also cause negative effects when there exists a high diversity among intra-domain samples. In this paper, we propose a novel Mixed Gate Control (MGC) model to fulfill 1) the division of samples into groups according to their commonalities and 2) the transfer of knowledge based on groups. We stack up multiple MGC layers into Multiple Layers MGC (ML-MGC) and apply it to a large-scale bundle retrieval system in a listed internet company. We conduct experiments on its own commercial dataset and two public real-world datasets. Experimental results show the superiority of our model against state-of-the-art methods for the CDR task. Finally, we present a case study to illustrate the groups identified by our model.
Embedded Image and Video Coding Algorithm Based on Adaptive Filtering Equation

Based on the improved adaptive filtering method, this paper conducts in-depth discussion and research on embedded graphics and video coding and chooses to improve the adaptive filtering algorithm from three aspects: starting point prediction, search template, and window partitioning. The algorithm is imported into the encoder for video capture and encoding. By capturing videos of different formats, resolutions, and times, the memory size of the video files collected before and after the algorithm optimization is compared, and the optimized algorithm occupies the memory space of the video file in the actual system. The conclusion of less and higher coding rates. The collected video information is stored on a personal computer equipped with a freeness, and external electronic devices only need to download and install the browser, and the collected video information can be accessed in the local area network through the protocol. The improved coding algorithm has higher coding efficiency and can reduce the storage space occupied by the video.
Exploration of Crime Detection Using Deep Learning

There is the need for a device to identify and forecast crimes at a complex period because of the escalation in the occurrence of crimes. The goal of this survey is to learn different deep learning techniques to help identify and forecast crimes using Deep Learning. Noticeable findings from this survey are that pre-processing becomes an essential activity as dataset instances have a significant amount of missing values, and violence does not arise consistently through metropolitan environments but concentrates in particular locations. Predicting crime hotspots is also a very important activity, and adding post-processing would help lower crime rates. The Deep Learning algorithm was commonly used in many areas, including image recognition and natural language processing. With fine tuning, the Deep Learning algorithm produces improved predictive performance. The overall detection depends on the crime data sets which could be found from the UCI library or any standard depository. Through Deep learning algorithm on the data set, it could be analyzed that how the crime patterns are in the particular demography. So, applying deep learning in crime prevention is highly advantageous and could be further reinforced by offering more resources to intelligent systems. This will certainly help to get better findings about crime through deep learnings. This paper explores mainly genetic algorithms to find the crime detection.
Adaptive Retraining for Neural Network Robustness in Classification

Neural network robustness has been a hot research topic since the discovery of adversarial examples. With the increasing threats posed by adversarial examples in safety-critical AI applications, the research problem of how to boost the robustness of neural networks has drawn even more attention in recent years. In this paper, we argue that existing methods for neural network robustness optimization primarily focus on increasing the minimum adversarial perturbation of individual datum while neglecting the purpose of the target machine learning task, i.e., classification. Consequently, we propose a neural network retraining method which implicitly enhances a model's capability in maximizing the minimum distance from data instances of all classes to the decision boundary. As demonstrated by the experiments, our method succeeds in improving the robustness of pre-trained neural networks within only a few retraining epochs while imposing little negative effect on classification accuracy, thus being applicable for online optimization of neural networks.
Understanding Online Safety Through Metaphors: UK Policymakers and Industry Discourses About the Internet

This article explores how metaphors about what the internet is inform policymaker and industry discourses, when they propose solutions on internet safety. More specifically, it analyzes documents by key players in this debate during a period when the UK government proposed direct regulation of online harms. The study finds that policy documents construct the internet primarily as a “place” that is separate from offline experience; and to a smaller extent as a “tool” that can be abused if it falls in the wrong hands. The article argues that these constructions obscure any links between online and offline risk, and that they legitimize solutions which may not take into account the social roots of online harms. It also suggests that the discourses of policymakers and SNS companies differ in the degree of agency they attribute to users, indicating a discrepancy in their approaches as direct regulation is introduced in the UK.
How can simplified requirements affect project efficiency – A case study in oil and gas

Abstract Requirements engineering is a constant challenge for companies executing complex projects. The oil and gas industry has been renowned for stringent stakeholder requirements driving costs. When the oil price plummeted six years ago, the industry had to adapt to make projects economically viable. Over the last four years, a major supplier to the industry has been executing three subsea production system projects as part of a frame agreement for a client. This case study investigates requirements engineering with a focus on cost savings. The paper examines data sources from the contractor and interviews with key project personnel. The main finding is that the contractor and client's efforts to simplify requirements have improved project efficiency. However, it has not been possible to quantify the exact benefit. Furthermore, the requirements engineering has been dependent on soft factors and collaboration during early study activities. This paper contributes with a description of a requirements engineering method. This is a collaborative method where the supplier adjusts the systems requirements, in close collaboration with the client, based on a detailed design in a very early phase of systems engineering. The research can also give additional insight into requirements engineering for other industries executing complex projects.