Reviewerly/exHarmony huggingface dataset:
Know Your Cognitive Environment! Mental Models as Crucial Determinant of Offloading Preferences

Human problem solvers possess the ability to outsource parts of their mental processing onto cognitive "helpers" (cognitive offloading). However, suboptimal decisions regarding which helper to recruit for which task occur frequently. Here, we investigate if understanding and adjusting a specific subcomponent of mental models-beliefs about task-specific expertise-regarding these helpers could provide a comparatively easy way to improve offloading decisions.Mental models afford the storage of beliefs about a helper that can be retrieved when needed.Arithmetic and social problems were solved by 192 participants. Participants could, in addition to solving a task on their own, offload cognitive processing onto a human, a robot, or one of two smartphone apps. These helpers were introduced with either task-specific (e.g., stating that an app would use machine learning to "recognize faces" and "read emotions") or task-unspecific (e.g., stating that an app was built for solving "complex cognitive tasks") descriptions of their expertise.Providing task-specific expertise information heavily altered offloading behavior for apps but much less so for humans or robots. This suggests (1) strong preexisting mental models of human and robot helpers and (2) a strong impact of mental model adjustment for novel helpers like unfamiliar smartphone apps.Creating and refining mental models is an easy approach to adjust offloading preferences and thus improve interactions with cognitive environments.To efficiently work in environments in which problem-solving includes consulting other people or cognitive tools ("helpers"), accurate mental models-especially regarding task-relevant expertise-are a crucial prerequisite.
Rough video conceptualization for real-time event precognition with motion entropy

This article defines a new methodology for pre-recognition of events with object motion analysis in a video without any prior knowledge. This unsupervised application is named as ‘conceptualization’. This conceptualization technique is also tested with real-time video data in an internet of things (IoT) architecture. The merits of rough sets in the framework of granular computing are explored to execute the task. The proposed method is designed for the video sequences that are acquired by simple static RGB sensors. Here the video sequences are granulated with our newly defined ‘motion granules’ and then those are modeled as rough sets over this granulation for moving object/ background estimation. Video conceptualization is performed afterwards by quantifying the approximation with a new measure, namely, motion entropy. The values obtained by this measure reflect the amount of uncertainty present in the motion of each individual moving object which enables precognition of events. The effectiveness of the proposed method is verified with extensive experiments in identifying the different motion patterns present in a video sequence. The frames with possibilities of events present therein are identified with this analysis. Both offline and real-time sequences are used for this verification. An IoT architecture is formed to test the proposed algorithm with physical devices in identifying the frames containing possible events.
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO

In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labelling effort among a large number of collaborators. For instance, this is being applied in the laureate laser interferometer gravitational waves observatory (LIGO), in order to detect glitches which might hinder the identification of true gravitational-waves. The crowdsourcing scenario poses new challenging difficulties, as it has to deal with different opinions from a heterogeneous group of annotators with unknown degrees of expertise. Probabilistic methods, such as Gaussian processes (GP), have proven successful in modeling this setting. However, GPs do not scale up well to large data sets, which hampers their broad adoption in real-world problems (in particular LIGO). This has led to the very recent introduction of deep learning based crowdsourcing methods, which have become the state-of-the-art for this type of problems. However, the accurate uncertainty quantification provided by GPs has been partially sacrificed. This is an important aspect for astrophysicists in LIGO, since a glitch detection system should provide very accurate probability distributions of its predictions. In this work, we first leverage a standard sparse GP approximation (SVGP) to develop a GP-based crowdsourcing method that factorizes into mini-batches. This makes it able to cope with previously-prohibitive data sets. This first approach, which we refer to as scalable variational Gaussian processes for crowdsourcing (SVGPCR), brings back GP-based methods to a state-of-the-art level, and excels at uncertainty quantification. SVGPCR is shown to outperform deep learning based methods and previous probabilistic ones when applied to the LIGO data. Its behavior and main properties are carefully analyzed in a controlled experiment based on the MNIST data set. Moreover, recent GP inference techniques are also adapted to crowdsourcing and evaluated experimentally.
Contextual Anomaly Detection in Solder Paste Inspection with Multi-Task Learning

In this article, we study solder paste inspection (SPI), an important stage that is used in the semiconductor manufacturing industry, where abnormal boards should be detected. A highly accurate SPI can substantially reduce human expert involvement, as well as reduce the waste in disposing of the boards in good condition. A key difference today is that because of increasing demand in board customization, the number of board types increases substantially and quantity of the boards produced in each type decreases. Thus, the previous approaches where a fine-tuned model is developed for each board type are no longer viable. Intrinsically, our problem is an anomaly detection problem. A major specialty in today’s SPI is that the target tasks for prediction cannot be fully pre-determined due to context changes during the solder paste printing stage. Our experiences show that a conventional approach to first define a set of tasks and train these tasks offline will lead to low accuracy. Here, we propose a novel multi-task approach, where the performance of all target tasks is ensured simultaneously. We note that the SPI process is streamlined and automatic, allowing the SPI time for only a few seconds. We propose a fast clustering algorithm that reuses existing models to avoid retraining and fine tune in the inference phase. We evaluate our approach using 3-month data collected from production lines. We show that we can reduce 81.28% of false alarms. This can translate to annual savings of $11.3 million.
A robust JPEG compression detector for image forensics

Identification of JPEG compressed images saved in uncompressed format (JPEG-U images) is an important issue in forensic analysis. The state-of-the-art JPEG compression detection methods fail to identify such images when subjected to post-processing/anti-forensic operations. In this paper, we propose a novel JPEG compression detector which is robust to post-processing and anti-forensic operations. The detector is based on the difference in the discrete cosine transform (DCT) coefficient distributions in the ac subbands of uncompressed images and JPEG-U images. We show theoretically and empirically that the probability of subband DCT coefficients which lie in the interval (−0.5,0.5) is significantly different for a JPEG-U and the corresponding uncompressed image. This difference is exploited to derive a detection statistic which is compared with a threshold to detect JPEG-U images. The detector makes use of calibration, a technique used in steganalysis, to obtain the detection statistic. The experimental results show that the proposed detector significantly outperforms the state-of-the-art detectors, especially in the presence of post-processing and anti-forensic operations.
Drivers and Persistence of Death in Conflicts: Global Evidence

We investigate persistence and determinants of deaths from conflicts in a sample of 163 countries for the period 2010–2015. The empirical evidence is based on the Generalized Method of Moments. First, the findings are contingent on income levels, religious domination, landlockedness, regional proximity, and legal origins. We find that the persistence of deaths in internal conflict is more apparent in coastal, French civil law, and Islam-oriented countries, compared to landlocked, English common law, Christian-oriented countries, respectively. Second, the following factors are generally responsible for driving deaths from internal conflicts: homicides, conflict intensity, and conflicts fought. Furthermore, incarcerations have negative effects on internal conflicts. Justifications for the established tendencies and policy implications are discussed.
Data Badges: Making an Academic Profile Through a DIY Wearable Physicalization

In this pictorial, we present the design and making process of Data Badges as they were deployed during a one-week academic seminar. Data Badges are customizable physical conference badges that invite participants to make their own independent and personalized expressions of their academic profile by choosing and assembling a collection of predefined physical tokens on a flat wearable canvas. As our modular and intuitive design approach allows the construction to occur as a shared, collective activity, Data Badges take advantage of the creative, affective, and social values that underlie physicalization and its construction to engage participants in reflecting on personal data. Among other unexpected phenomena, we noticed how the freedom of assembly and interpretation encouraged a variety of appropriations, which expanded its intended representational space from fully representative to more resistive and provocative forms of data expression.
Possessions and memories

People often acquire souvenirs and photographs to facilitate remembering, but possessions and memories can relate to each other in a variety of ways. This review paper presents four different connection types found between meaningful things in our everyday lives and our personal memories. Each connection type either focuses on possessions or memories and the connection between the two is either active or lost. These perspectives will be detailed through examples of studies and design cases from different fields and research areas. More studies have been found focusing on existing connections between possessions and memories, such as in human-computer interaction, design, material culture, psychology and marketing, than those lost, which were specifically focused around ageing, forgetting, heirlooms, identity and hoarding behaviour. Our review of connections between possessions and memories accumulate to suggest the attachment people ascribe to certain possessions is mirrored by the ability of objects to fulfil people's desire to preserve, embody, showcase and recollect certain memories.
Improving Multi-Agent Generative Adversarial Nets with Variational Latent Representation

Generative adversarial networks (GANs), which are a promising type of deep generative network, have recently drawn considerable attention and made impressive progress. However, GAN models suffer from the well-known problem of mode collapse. This study focuses on this challenge and introduces a new model design, called the encoded multi-agent generative adversarial network (E-MGAN), which tackles the mode collapse problem by introducing the variational latent representations learned from a variable auto-encoder (VAE) to a multi-agent GAN. The variational latent representations are extracted from training data to replace the random noise input of the general multi-agent GANs. The generator in E-MGAN employs multiple generators and is penalized by a classifier. This integration guarantees that the proposed model not only enhances the quality of generated samples but also improves the diversity of generated samples to avoid the mode collapse problem. Moreover, extensive experiments are conducted on both a synthetic dataset and two large-scale real-world datasets. The generated samples are visualized for qualitative evaluation. The inception score (IS) and Fréchet inception distance (FID) are adopted to measure the performance of the model for quantitative assessment. The results confirmed that the proposed model achieves outstanding performances compared to other state-of-the-art GAN variants.
The Design and Implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning

In this paper, we proposed a transfer learning-based English language learning chatbot, whose output generated by GPT-2 can be explained by corresponding ontology graph rooted by finetuning dataset.We design three levels for systematically English learning, including phonetics level for speech recognition and pronunciation correction, semantic level for specific domain conversation, and the simulation of "free-style conversation" in English -the highest level of language chatbot communication as 'free-style conversation agent'.For academic contribution, we implement the ontology graph to explain the performance of free-style conversation, following the concept of XAI (Explainable Artificial Intelligence) to visualize the connections of neural network in bionics, and explain the output sentence from language model.From implementation perspective, our Language Learning agent integrated the mini-program in WeChat as front-end, and fine-tuned GPT-2 model of transfer learning as back-end to interpret the responses by ontology graph.
Part 1: The need for peer review in digital forensics

The importance of peer review in the field of digital forensics cannot be underestimated as it often forms the primary, and sometimes only form of quality assurance process an organisation will apply to their practitioners' casework. Whilst there is clear value in the peer review process, it remains an area which is arguably undervalued and under-researched, where little academic and industrial commentary can be found describing best practice approaches. This work forms the first of a two part series discussing why the digital forensics discipline and its organisations should conduct peer review in their laboratories, what it should review as part of this process, and how this should be undertaken. Here in part one, a critical review of the need to peer review is offered along with a discussion of the limitations of existing peer review mechanisms. Finally, the 'Peer Review Hierarchy' is offered, outlining the seven levels of peer review available for reviewing practitioner findings.
Multi-view Consensus Proximity Learning for Clustering

Most proximity-based multi-view clustering methods are sensitive to the initial proximity matrix, where the clustering performance is quite unstable when using different initial proximity matrixes. This problem is defined as the initial value sensitivity problem. Since clustering is an unsupervised learning task, it is unrealistic to tune the initial proximity matrix. Thus, how to overcome the initial value sensitivity problem is a significant but unsolved issue in the proximity-based multi-view clustering. To this end, this paper proposes a novel multi-view proximity learning method, named multi-view consensus proximity learning (MCPL). On the one hand, by integrating the information of all views in a self-weighted manner and giving a rank constraint on the Laplacian matrix, the MCPL method learns the consensus proximity matrix to directly reflect the clustering result. On the other hand, different from most multi-view proximity learning methods, in the proposed MCPL method, the data representatives rather than the original data objects are adopted to learn the consensus proximity matrix. The data representatives will be updated in the process of the proximity learning so as to weaken the impact of the initial value on the clustering performance. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method.
Agricultural UAV Path Planning Based on Improved A* and Gravity Search Mixed Algorithm

In order to ensure the accuracy, efficiency and energy saving of Unmanned Aerial Vehicles (UAVs) in irregular working areas, this paper has carried out research on its path planning methods. Taking the heading angle and return point of agricultural UAVs as the optimization objectives, the plan was made by improving the A* and gravitational search algorithm to plan the optimal path. First, the execution process of the algorithm is analysed, the application model is solved, and then the simulation experiment is performed through MATLAB. The path calculated by the proposed method was also compared with the unplanned and gravitational search algorithm planned path respectively, which showed the non-spraying distance of the proposed method was reduced by 23.18% and 5.83%, while the excess coverage of pesticide spraying was reduced by 64.47% and 64.47%. The study indicated that the proposed method which could produce paths with less working time was a reasonable, feasible and useful solution for the path planning problem of the plant protection UAV.
Knowledge Distillation for Optimization of Quantized Deep Neural Networks

Knowledge distillation (KD) technique that utilizes a pretrained teacher model for training a student network is exploited for the optimization of quantized deep neural networks (QDNNs). We consider the choice of the teacher network and also investigate the effect of hyperparameters for KD. We employ several large floating-point and quantized models as the teacher network. The experiment shows that the softmax distribution produced by the teacher network is more important than its performance for effective KD training. Since the softmax distribution of the teacher network can be controlled by KD's hyperparameters, we analyze the interrelationship of each KD component for quantized DNN training. Our experiments show that even a small teacher model can achieve the same distillation performance as a large teacher model. We also propose the gradual soft loss reduction (GSLR) technique which controls the mixing ratio of hard and soft losses during training for robust KD based QDNN optimization.
Intelligent HMI and Control for Steering a Powered Wheelchair Using a Raspberry Pi Microcomputer

This paper presents a new technique for controlling powered wheelchairs. A Raspberry Pi microcomputer is used to assist in controlling direction. A Raspberry Pi is inserted between user input switches and powered wheelchair motors to create a more intelligent Human Machine Interface (HMI). An electronic circuit is created that consists of an ultrasonic sensor array and a set of control relays. The sensors provided information about obstacles surrounding the wheelchair. Python programming language was used to create a code that digitized the output from the user switches and assessed information provided by the ultrasonic sensor array. The code was loaded onto a Raspberry Pi and the Raspberry Pi controlled voltages supplied to the motors. Tests were conducted and results showed that the new system can successfully assist a wheelchair user in avoiding obstacles. The system can be used as an intelligent interface between any input device or sensor system and wheelchair motors.
Text Mining in the Domain of Plant Genetic Resources

Upcoming is the construction of informational and social spaces which integrate heterogeneous technologies and various approaches to the collection, storage and processing of information related to the plant genetic resources of Bulgaria. In the present paper a multistage algorithm for automatic analysis of documents from the area of plant genetic resources is proposed. Determination of the significant words in the document and the type of its content is carried out based on the object-oriented ontologies in the field. By applying software instruments for classification, testing and evaluation, the documents are classified into related and not related to the studied subject area - agriculture and plant genetic resources of Bulgaria.
A Blockchain-Based Model of GenBank Store System

The article introduces a blockchain-based model for development of the GenBank Store module as part of the "Agriculture 2.0 Plovdiv" system.
Adversarial feature distribution alignment for semi-supervised learning

Training deep neural networks with only a few labeled samples can lead to overfitting. This is problematic in semi-supervised learning where only a few labeled samples are available. In this paper, we show that a consequence of overfitting in SSL is feature distribution misalignment between labeled and unlabeled samples. Hence, we propose a new feature distribution alignment method. Our method is particularly effective when using only a small amount of labeled samples. We test our method on CIFAR-10, SVHN and LSUN. On SVHN we achieve a test error of 3.88% (250 labeled samples) and 3.39% (1000 labeled samples), which is close to the fully supervised model 2.89% (73k labeled samples). In comparison, the current SOTA achieves only 4.29% and 3.74%. On LSUN we achieve superior results than a state-of-the- art method even when using 100× less unlabeled samples (500 labeled samples). Finally, we provide a theoretical insight why feature distribution misalignment occurs and show that our method reduces it.
Few-Shot Human Activity Recognition on Noisy Wearable Sensor Data

Most existing wearable sensor-based human activity recognition (HAR) models are trained on substantial labeled data. It is difficult for HAR to learn new-class activities unseen during training from a few samples. Very few researches of few-shot learning (FSL) have been done in HAR to address the above problem, though FSL has been widely used in computer vision tasks. Besides, it is impractical to annotate sensor data with accurate activity labels in real-life applications. The noisy labels have great negative effects on FSL due to the limited samples. The weakly supervised few-shot learning in HAR is challenging, significant but rarely researched in existing literature. In this paper, we propose an end-to-end Weakly supervised Prototypical Networks (WPN) to learn more latent information from noisy data with multiple instance learning (MIL). In MIL, the noisy instances (subsequences of segmentation) have different labels from the bag's (segmentation's) label. The prototype is the center of the instances in WPN rather than less discriminative bags, which determines the bag-level classification accuracy. To get the most representative instance-level prototype, we propose two strategies to refine the prototype by selecting high-probability instances same as their bag's label iteratively based on the distance-metric. The model is trained by minimizing the instance-level loss function and infers the final bag-level labels from instance-level labels. In the experiments, our proposals outperform existing approaches and achieve higher average ranks.