Reviewerly/exHarmony huggingface dataset:
Unsupervised Deep Learning to Detect Agitation From Videos in People With Dementia

Behavioural symptoms of dementia present a significant risk within Long Term Care (LTC) homes, which face difficulties supporting residents and monitoring their safety with limited staffing resources. Many LTC facilities have installed video surveillance systems in common areas that can help staff to observe residents; however, typically these video streams are not monitored. In this paper, we present the development of a computer vision algorithm to use these video streams to detect episodes of clinically important agitation in people with dementia. Given that episodes of agitation are rare in comparison to normal behaviours, we formulated this as an anomaly detection problem. This involves using the video camera to monitor the scene rather than tracking individuals. We developed a customized spatio-temporal convolution autoencoder that is trained on the normal behaviours and then identified agitation during testing as anomalous behaviour. We present a proof-of-concept using video data collected from a specialized dementia unit and annotated for agitation events. We trained the unsupervised neural network on approximately 24 hours of normal activities and tested on 11 hours of videos containing both normal activities and agitation events, and obtained an area under the curve of the receiver operating characteristic curve of 0.754. This research paves the way for leveraging existing surveillance infrastructure in LTC and other mental health settings to detect agitation or aggression, with the potential for improved health and safety.
Variations on a Theme by Massey

In 1994, Jim Massey proposed the guessing entropy as a measure of the difficulty that an attacker has to guess a secret used in a cryptographic system, and established a well-known inequality between entropy and guessing entropy. Over 15 years before, in an unpublished work, he also established a well-known inequality for the entropy of an integer-valued random variable of given variance. In this paper, we establish a link between the two works by Massey in the more general framework of the relationship between discrete (absolute) entropy and continuous (differential) entropy. Two approaches are given in which the discrete entropy (or Rényi entropy) of an integer-valued variable can be upper bounded using the differential (Rényi) entropy of some suitably chosen continuous random variable. As an application, lower bounds on guessing entropy and guessing moments are derived in terms of entropy or Rényi entropy (without side information) and conditional entropy or Arimoto conditional entropy (when side information is available).
Named Entity Recognition for Entity Linking: What Works and What’s Next

Entity Linking (EL) systems have achieved impressive results on standard benchmarks, mainly thanks to the contextualized representations provided by recent pretrained language models.However, such systems still require massive amounts of data -millions of labeled examples -to perform at their best, with training times that often exceed several days, especially when limited computational resources are available.In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data.More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities.We release our software -code and model checkpoints -at https://github.com/Babelscape/ner4el.
Multiple weak supervision for short text classification

Abstract For short text classification, insufficient labeled data, data sparsity, and imbalanced classification have become three major challenges. For this, we proposed multiple weak supervision, which can label unlabeled data automatically. Different from prior work, the proposed method can generate probabilistic labels through conditional independent model. What’s more, experiments were conducted to verify the effectiveness of multiple weak supervision. According to experimental results on public dadasets, real datasets and synthetic datasets, unlabeled imbalanced short text classification problem can be solved effectively by multiple weak supervision. Notably, without reducing precision , recall , and F1-score can be improved by adding distant supervision clustering, which can be used to meet different application needs.
Robust Quantum Metrology With Explicit Symmetric States

Quantum metrology is a promising practical use case for quantum technologies, where physical quantities can be measured with unprecedented precision. In lieu of quantum error correction procedures, near term quantum devices are expected to be noisy, and we have to make do with noisy probe states. We prove that, for a set of carefully chosen symmetric probe states that lie within certain quantum error correction codes, quantum metrology exhibits an advantage over classical metrology even after the probe states are corrupted by a constant number of erasure and dephasing errors. These probe states prove useful for robust metrology not only in the NISQ regime, but also in the asymptotic setting where they achieve Heisenberg scaling. This brings us closer towards making robust quantum metrology a technological reality.
Derived blockchain architecture for security-conscious data dissemination in edge-envisioned Internet of Drones ecosystem

Abstract Internet of Drones (IoD) facilitates the autonomous operations of drones into every application (warfare, surveillance, photography, etc) across the world. The transmission of data (to and fro) related to these applications occur between the drones and the other infrastructure over wireless channels that must abide to the stringent latency restrictions. However, relaying this data to the core cloud infrastructure may lead to a higher round trip delay. Thus, we utilize the cloud close to the ground, i.e., edge computing to realize an edge-envisioned IoD ecosystem. However, as this data is relayed over an open communication channel, it is often prone to different types of attacks due to it wider attack surface. Thus, we need to find a robust solution that can maintain the confidentiality, integrity, and authenticity of the data while providing desired services. Blockchain technology is capable to handle these challenges owing to the distributed ledger that store the data immutably. However, the conventional block architecture pose several challenges because of limited computational capabilities of drones. As the size of blockchain increases, the data flow also increases and so does the associated challenges. Hence, to overcome these challenges, in this work, we have proposed a derived blockchain architecture that decouples the data part (or block ledger) from the block header and shifts it to off-chain storage. In our approach, the registration of a new drone is performed to enable legitimate access control thus ensuring identity management and traceability. Further, the interactions happen in the form of transactions of the blockchain. We propose a lightweight consensus mechanism based on the stochastic selection followed by a transaction signing process to ensure that each drone is in control of its block. The proposed scheme also handles the expanding storage requirements with the help of data compression using a shrinking block mechanism. Lastly, the problem of additional delay anticipated due to drone mobility is handled using a multi-level caching mechanism. The proposed work has been validated in a simulated Gazebo environment and the results are promising in terms of different metrics. We have also provided numerical validations in context of complexity, communication overheads and computation costs.
Using virtual reality to increase charitable donations

Marketer interest in using virtual reality (VR) as a persuasion tactic continues to rise. Notably, one sector at the forefront of utilizing this tactic for persuasive means is nonprofit marketing. Many charities have devoted considerable resources to creating VR appeals under the assumption that this medium will increase donations over and above present tactics. However, research has not yet examined the persuasive consequences VR may provide over more traditional channels. This research seeks to understand the opportunities and limitations this emerging tactic can offer marketers. Specifically, we examine the donation effectiveness of three real VR charitable appeals by assessing actual donation behaviors, and find that VR appeals increase donations compared to a two-dimensional (2D) format. This work addresses a timely and relevant issue for practitioners and opens doors to future research investigating VR's applications to marketing.The online version contains supplementary material available at 10.1007/s11002-021-09601-8.
A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).
A survey on effects of adding explanations to recommender systems

Abstract Explainable recommendations become essential when we need to improve the performance of recommendations and to increase user confidence. Explanations are effective when end users can build a complete and correct mental representation of the inferential process of a recommender system. This paper presents our view on the background regarding the implications of explainability applied to recommender systems. Our work contributes to the better understanding of the concept of explainable recommendation and it offers a broader picture of the development of further research in this field. Additionally, we contribute by providing a better understanding of the concept of human‐centered evaluation of explainable recommender systems.
A Large-Scale Dataset for Empathetic Response Generation

Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset.This is especially the case for response generation where emotion plays an important role.However, existing empathetic datasets remain small, delaying research efforts in this area, for example, the development of emotion-aware chatbots.One main technical challenge has been the cost of manually annotating dialogues with the right emotion labels.In this paper, we describe a large-scale silver dataset consisting of 1M dialogues annotated with 32 fine-grained emotions, eight empathetic response intents, and the Neutral category.To achieve this goal, we have developed a novel data curation pipeline starting with a small seed of manually annotated data and eventually scaling it to a satisfactory size.We compare its quality against a state-of-the-art gold dataset using offline experiments and visual validation methods.The resultant procedure can be used to create similar datasets in the same domain as well as in other domains. 1
A Motion Planning Method for Automated Vehicles in Dynamic Traffic Scenarios

We propose a motion planning method for automated vehicles (AVs) to complete driving tasks in dynamic traffic scenes. The proposed method aims to generate motion trajectories for an AV after obtaining the surrounding dynamic information and making a preliminary driving decision. The method generates a reference line by interpolating the original waypoints and generates optional trajectories with costs in a prediction interval containing three dimensions (lateral distance, time, and velocity) in the Frenet frame, and filters the optimal trajectory by a series of threshold checks. When calculating the feasibility of optional trajectories, the cost of all optional trajectories after removing obstacle interference shows obvious axisymmetric regularity concerning the reference line. Based on this regularity, we apply the constrained Simulated Annealing Algorithm (SAA) to improve the process of searching for the optimal trajectories. Experiments in three different simulated driving scenarios (speed maintaining, lane changing, and car following) show that the proposed method can efficiently generate safe and comfortable motion trajectories for AVs in dynamic environments. Compared with the method of traversing sampling points in discrete space, the improved motion planning method saves 70.23% of the computation time, and overcomes the limitation of the spatial sampling interval.
ROBOT–BRAND FIT THE INFLUENCE OF BRAND PERSONALITY ON CONSUMER REACTIONS TO SERVICE ROBOT ADOPTION

Can every brand benefit from adopting service robots? To tackle this important question, we examined the interactive effects of brand personality (sincere vs. exciting) and service robot type (high-contact vs. low-contact) on customer reactions to service robot implementation. Results from three experimental studies indicate that customers tend to react negatively to high-contact robots when the brand had a sincere (vs. exciting) personality. This tendency is driven by the poor perceived fit between the sincere brand personality and the implementation of high-contact robots. However, such brand personality effects are mitigated in the adoption of low-contact robots. For a sincere brand adopting high-contact robots, we suggest that signaling warmth can enhance the perceived brand-robot fit and thereby reduce negative customer reactions.
Adaptive Professional Development during the Pandemic

In Sweden, upper secondary school teachers made a swift transition into emergency remote teaching in 2020 due to the outbreak of covid-19. This paper reports on a Design-Based Research intervention in which professional development was designed using the Blended Learning Adoption framework, to support teachers to develop their teaching practices online. Twenty-six teachers participated in the intervention which spanned six months. Data was analysed using thematic analysis. Key results revealed that the pandemic had become an impetus for change, for many teachers, but far from all. Emerging teaching practices in synchronous online learning included: inviting special needs pedagogues in parallel breakout rooms, and grouping and re-grouping students when facilitating varied collaboration. Apart from realising new potentials of online teaching and learning, teachers identified emerging challenges such as: new ways of cheating, ethical aspects of accessing students’ private homes via cameras and a lack of guidelines on managing disengagement. Conclusively, teacher’s professional development and new experiences elicit new practices that could benefit teachers after the pandemic. Professional development during uncertain times and design principles supporting intervention ownership transfer are discussed.
Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting

Person re-identification (ReID) plays a crucial role in video surveillance with the aim to search a specific person across disjoint cameras, and it has progressed notably in recent years. However, visible cameras may not be able to record enough information about the pedestrian’s appearance under the condition of low illumination. On the contrary, thermal infrared images can significantly mitigate this issue. To this end, combining visible images with infrared images is a natural trend, and are considerably heterogeneous modalities. Some attempts have recently been contributed to visible-infrared person re-identification (VI-ReID). This paper provides a complete overview of current VI-ReID approaches that employ deep learning algorithms. To align with the practical application scenarios, we first propose a new testing setting and systematically evaluate state-of-the-art methods based on our new setting. Then, we compare ReID with VI-ReID in three aspects, including data composition, challenges, and performance. According to the summary of previous work, we classify the existing methods into two categories. Additionally, we elaborate on frequently used datasets and metrics for performance evaluation. We give insights on the historical development and conclude the limitations of off-the-shelf methods. We finally discuss the future directions of VI-ReID that the community should further address.
Fending off Delta – Hospital measures to reduce nosocomial transmission of COVID-19

Following the emergence of the Delta variant of SARS-CoV-2 in Singapore, our hospital experienced a Delta-linked ward cluster. In this study, we review the enhanced strategies in preventing nosocomial transmission of COVID-19 following widespread community transmission of the Delta variant.We conducted a cohort study on exposures to unexpected COVID-19 cases for which contact tracing was initiated from June 2021 to October 2021. Strategies evaluated included upgraded personal protective equipment (PPE) and rostered routine testing (RRT) for staff and patients, surveillance of staff with acute respiratory illness (ARI), and expanded quarantining and testing for contacts of identified cases.From 193 unexpected COVID-19 exposures, 2,573 staff, 542 patients, and 128 visitor contacts were traced. Four staff contacts subsequently had SARS-CoV-2 infection. Two were likely from exposure in community settings, whereas 2 had exposure to the same COVID-19 positive staff in the hospital, forming the only hospital cluster. One inpatient had a nosocomial infection, possibly from visitors. The SARS-CoV-2 detection rate among staff was 0.3% (of 11,200 staff) from biweekly RRT and 2.5% (of 3,675 staff) from ARI surveillance.Enhanced hospital measures, including upgraded PPE and RRT for staff and patients, staff sickness surveillance, and more rigorous management of contacts of COVID-19 cases, were likely to have reduced nosocomial transmission amid the Delta variant.
Comparative Study of Long Document Classification

The amount of information stored in the form of documents on the internet has been increasing rapidly. Thus it has become a necessity to organize and maintain these documents in an optimum manner. Text classification algorithms study the complex relationships between words in a text and try to interpret the semantics of the document. These algorithms have evolved significantly in the past few years. There has been a lot of progress from simple machine learning algorithms to transformer-based architectures. However, existing literature has analyzed different approaches on different data sets thus making it difficult to compare the performance of machine learning algorithms. In this work, we revisit long document classification using standard machine learning approaches. We benchmark approaches ranging from simple Naive Bayes to complex BERT on six standard text classification datasets. We present an exhaustive comparison of different algorithms on a range of long document datasets. We re-iterate that long document classification is a simpler task and even basic algorithms perform competitively with BERT-based approaches on most of the datasets. The BERT-based models perform consistently well on all the datasets and can be blindly used for the document classification task when the computations cost is not a concern. In the shallow model's category, we suggest the usage of raw BiLSTM + Max architecture which performs decently across all the datasets. Even simpler Glove + Attention bag of words model can be utilized for simpler use cases. The importance of using sophisticated models is clearly visible in the IMDB sentiment dataset which is a comparatively harder task.
Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds

Abstract Time series data generated by manufacturing machines during processing is widely used in mass part production to assess if processes run without errors. Systems that make use of this data use machine learning approaches for flagging a time series as a deviation from normal behaviour. In single part production, the amount of data generated is not sufficient for learning-based classification. Here, methods often focus on global signal variance but have trouble finding anomalies that present as local signal deviations. The referencing of the process states of the machine is usually performed by state indexing which, however, is not sufficient in highly flexible production plants. In this paper, a system that learns granular patterns in time series based on mean shift clustering is used for detecting processing segments in varying machine conditions. An anomaly detection then finds deviating patterns based on the previously identified processing segments. The anomalies can then be labeled by a human-in-the-loop approach for enabling future anomaly classification using a combination of machine learning algorithms. The method of anomaly detection is validated using an industrial machine tool and multiple test series.
Efficient Card-Based Majority Voting Protocols

Abstract Card-based cryptography is a variety of secure multiparty computation (MPC). Recently, a new technique called private operations was introduced because the protocol can be implemented with fewer cards than that by using the conventional technique called the shuffle. For example, Nakai et al. showed that if the private operations are available, secure computations of AND and OR operations for two inputs can be realized simultaneously by using four cards, and the protocol is applied to a four-card majority voting protocol with three inputs. This paper shows that only three cards are sufficient to construct a majority voting protocol with three inputs. Specifically, we propose two constructions of three-input majority voting protocols. One is a protocol assuming that players can announce their output, and the other is not allowed. Compared to Nakai et al.’s protocol, the protocol with the announcement is realized without any additional private operations and communications. On the other hand, the second construction requires two more private operations and communications because it removes the assumption on the announcement from the first construction. More importantly, the idea of the second protocol can be extended to an n -input majority voting protocol with n cards, which is the main result of this paper.