Reviewerly/exHarmony huggingface dataset:
Lognormal and Mixed Gaussian–Lognormal Kalman Filters

Abstract In this paper we present the derivation of two new forms of the Kalman filter equations; the first is for a pure lognormally distributed random variable, while the second set of Kalman filter equations will be for a combination of Gaussian and lognormally distributed random variables. We show that the appearance is similar to that of the Gaussian-based equations, but that the analysis state is a multivariate median and not the mean. We also show results of the mixed distribution Kalman filter with the Lorenz 1963 model with lognormal errors for the background and observations of the z component, and compare them to analysis results from a traditional Gaussian-based extended Kalman filter and show that under certain circumstances the new approach produces more accurate results.
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy

We study the privacy-utility trade-off in the context of metric differential privacy. Ghosh et al. introduced the idea of universal optimality to characterise the “best” mechanism for a certain query that simultaneously satisfies (a fixed) <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{E-}$</tex> differential privacy constraint whilst at the same time providing better utility compared to any other s-differentially private mechanism for the same query. They showed that the Geometric mechanism is universally optimal for the class of counting queries. On the other hand, Brenner and Nissim showed that outside the space of counting queries, and for the Bayes risk loss function, no such universally optimal mechanisms exist. Except for universal optimality of the Laplace mechanism, there have been no generalisations of these universally optimal results to other classes of differentially-private mechanisms. In this paper we use metric differential privacy and quantitative information flow as the fundamental principle for studying universal optimality. Metric differential privacy is a generali-sation of both standard (i.e., central) differential privacy and local differential privacy, and it is increasingly being used in various application domains, for instance in location privacy and in privacy preserving machine learning. As do Ghosh et al. and Brenner and Nissim, we measure utility in terms of loss functions, and we interpret the notion of a privacy mechanism as an information-theoretic channel satisfying constraints defined by ε-differcntlal privacy and a metric meaningful to the underlying state space. Using this framework we are able to clarify Nissim and Brenner's negative results by (a) that in fact all privacy types contain optimal mechanisms relative to certain kinds of non-trivial loss functions, and (b) extending and generalising their negative results beyond Bayes risk specifically to a wide class of non-trivial loss functions. Our exploration suggests that universally optimal mechanisms are indeed rare within privacy types. We therefore propose weaker universal benchmarks of utility called privacy type ca-pacities. We show that such capacities always exist and can be computed using a convex optimisation algorithm. We illustrate these ideas on a selection of examples with several different underlying metrics.
Dual-level contrastive learning for unsupervised person re-identification

Unsupervised person re-identification (re-ID) has drawn increasing attention in the community because it is more data-friendly when applied in the real world. Most existing works leverage instance discrimination learning to guide the model to learn image features for person matching. However, the instance discrimination may cause unexpected repulsion among similar samples, which makes the unsupervised feature learning unstable. To address this problem, we propose a dual-level contrastive learning (DLCL) framework to mine both the intra-instance and inter-instance similarities. The DLCL framework consists of two tasks: instance-instance contrastive learning (IICL) and instance-community contrastive learning (ICCL). The IICL aims to mine the intra-instance similarity via pulling an original sample and its augmented versions closer and pushing different instances away. The ICCL is proposed to capture the inter-instance similarity by attracting similar instances to the same sample community, which can reduce the unexpected repulsion brought by instance discrimination. The combination of IICL and ICCL can enable the model to learn more robust and discriminative image features. Extensive experimental results on Market-1501 and DukeMTMC-reID indicate the effectiveness of our method for unsupervised person re-ID.
An Efficient Pedestrian Detection for Realtime Surveillance Systems Based on Modified YOLOv3

Pedestrian detection is an important branch of object detection due to its various applications. It plays a vital role in many fields such as intelligent surveillance systems. The recognition, identification and tracking modules of surveillance are based on efficient and accurate pedestrian detection. Our paper proposes an efficient model to solve real-time pedestrian detection with high accuracy based on modified ShuffleNet and YOLOv3 models. We provide a method to pick the dimensions and number of anchor boxes for predicting bounding boxes accurately. Then we use two improved shuffle units to lightweight the backbone of YOLOv3, which reduces the 67.5% floating point operations per second (FLOPs) and 65.1% parameters. We validate our model on CrowdHuman detection data set and get 62.7 mAP for face and 62.0 mAP person with 0.748 average IOU. Our network processes images in real-time at 186.1 frames per second for network and 12.5 frames per second for the entire model on CrowdHuman.
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain

Navigating off-road with a fast autonomous vehicle depends on a robust perception system that differentiates traversable from non-traversable terrain. Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert. This requires a significant investment in human time, assumes correct expert classification, and small details can lead to misclassification. To address these challenges, we propose a method for predicting high- and low-risk terrains from only past vehicle experience in a self-supervised fashion. First, we develop a tool that projects the vehicle trajectory into the front camera image. Second, occlusions in the 3D representation of the terrain are filtered out. Third, an autoencoder trained on masked vehicle trajectory regions identifies low- and high-risk terrains based on the reconstruction error. We evaluated our approach with two models and different bottleneck sizes with two different training and testing sites with a four-wheeled off-road vehicle. Comparison with two independent test sets of semantic labels from similar terrain as training sites demonstrates the ability to separate the ground as low-risk and the vegetation as high-risk with 81.1% and 85.1% accuracy.
Domain Model Extraction from User-authored Scenarios and Word Embeddings

Domain models are used by requirements analysts to rationalize domain phenomena into discrete entities that drive requirements elicitation and analysis. Domain models include entities, actors or agents, their actions, and desired qualities assigned to states in the domain. Domain models are acquired through a wide range of sources, including interviews with subject matter experts, and by analyzing text-based scenarios, regulations and policies. Requirements automation to assist with elicitation or text analysis can be supported using masked language models (MLM), which have been used to learn contextual information from natural language sentences and transfer this learning to natural language processing (NLP) tasks. The MLM can be used to predict the most likely missing word in a sentence, and thus be used to explore domain concepts encoded in a word embedding. In this paper, we explore an approach of extracting domain knowledge from user-authored scenarios using typed dependency parsing techniques. We also explore the efficacy of a complementary approach of using a BERT-based MLM to identify entities and associated qualities to build a domain model from a single-word seed term.
Selective Disclosure in Self-Sovereign Identity based on Hashed Values

Every person has associated a set of attributes that need to be shown to perform daily operations, such as the ones in personal identity card. The Self Sovereign Identity framework brings this to the digital world, giving to the user full responsibility of their own attributes. Each user receives a set of attributes is paired with as a Verifiable Credential, which could be issued, for example, by a municipality as a digitally signed ID card. However, it is not possible to disclose only one attribute of the ID card without invalidating the signature, therefore Selective Disclosure approaches have been defined to address this problem. This paper describes a selective disclosure method based on hashing, showing that the method is secure and applicable to the real world due to its low requirement in terms of space and time to create and verify a credential.
Automatic Driving Scene Target Detection Algorithm Based on Improved YOLOv5 Network

Target detection algorithm has become more and more important in the field of automatic driving. It is the most difficult to balance the high precision target detection and high speed processing time in target detection algorithm. Therefore, this paper proposes an improved YOLOv5 algorithm to comprehensively improve the accuracy and operation speed of target detection in automatic driving scenarios. In this paper, DCN network is introduced in the neural network model to replace the CNN layer in YOLOv5, and the fine feature extraction of images is realized in the backbone feature extraction layer. In addition, in the selection of anchor points, K-means++ clustering algorithm is adopted in this paper to replace the K-means algorithm in the original algorithm to achieve faster and more accurate selection of anchor frames. At the same time, DIOU is introduced as the regression loss of the prediction box, which accelerates the convergence speed of the algorithm. The improved algorithm has improved mAP on KITTI dataset and COCO dataset. Therefore, this paper can achieve target detection that is more suitable for automatic driving scenarios.
Toward Dynamic Difficulty Adjustment with Audio Cues by Gaussian Process Regression in a First-Person Shooter

This study presents an in-game audio-cue setting approach using Gaussian Process Regression (GPR) to provide dynamic difficulty adjustment (DDA) in a first-person shooter game. A novel difficulty-recommendation method is proposed to set our GPR goal for a better personalized audio cue setting. Recent studies showed that audio cues could give the player significant affective changes and influence the player’s performance. In addition, other studies showed that it is effective to train the player’s in-game ability by adjusting audio cues. However, no research has used audio cues as the primary factor in determining game difficulty. Experimental results show that our game can reach a recommended difficulty within a few plays.
Towards IoT Rejuvenation: a Study on HY-SRF05 Ultrasonic Sensor Ageing for Intelligent Street Pole Lamp Control in a Smart City

Nowadays, the Internet of Things (IoT) is widely adopted. To push-down costs, a possible solution can be represented by the large-scale adoption of low-cost sensors that, unfortunately, present several issues including sensor ageing that may cause, over time, failures in IoT systems due to erroneous collected data. In our opinion, a possible solution is IoT rejuvenation, a proactive cost-effective technique that can contrast the inevitable ageing of IoT systems guaranteeing the accuracy of collected data over time. Specifically, the purpose of this paper is to experimentally demonstrate the seriousness of the IoT ageing issue. To achieve such a goal, we consider a smart city scenario including intelligent street pole lamps equipped with low-cost ultrasonic sensors able to switch on lights when a vehicle is detected. Moreover, we pave the way toward IoT rejuvenation by discussing the perspectives of a Function as a Service (FaaS) based approach acting at the Edge.
Extending a security ontology framework to model CAPEC attack paths and TAL adversary profiles

Security evaluation can be performed using a variety of analysis methods, such as attack trees, attack graphs, threat propagation models, stochastic Petri nets, and so on. These methods analyze the effect of attacks on the system, and estimate security attributes from different perspectives. However, they require information from experts in the application domain for properly capturing the key elements of an attack scenario: i) the attack paths a system could be subject to, and ii) the different characteristics of the possible adversaries. For this reason, some recent works focused on the generation of low-level security models from a high-level description of the system, hiding the technical details from the modeler.In this paper we build on an existing ontology framework for security analysis, available in the ADVISE Meta tool, and we extend it in two directions: i) to cover the attack patterns available in the CAPEC database, a comprehensive dictionary of known patterns of attack, and ii) to capture all the adversaries’ profiles as defined in the Threat Agent Library (TAL), a reference library for defining the characteristics of external and internal threat agents ranging from industrial spies to untrained employees. The proposed extension supports a richer combination of adversaries’ profiles and attack paths, and provides guidance on how to further enrich the ontology based on taxonomies of attacks and adversaries.
Haptics in VR Using Origami-Augmented Drones

Virtual reality (VR) aims to make the human-computer interaction experience more immersive. Without the sensation of force and proper haptic feedback, however, the illusion of presence often breaks when a user tries to touch a virtual object. We present a prototype that provides haptic feedback in VR using origami carried by a drone. The drone delivers origami to the user's hand when that user is about to touch a virtual object. The haptic experience can easily be modified by changing the origami to other shapes and paper types. Our work demonstrates a novel, customisable, and low-cost solution to enable VR haptics.
News Article Classification using a Transfer Learning Approach

The increasing proliferation of electronic files in recent decades has been addressed by significant improvements in Natural Language Processing. As the text data generated is quite large, employing sophisticated algorithms that can process massive data within seconds is needed. Machine learning algorithms seem to be promising in the case of text processing and classifying articles. Still, it lacks automated feature extraction. Moreover, the advancements lead to deep learning algorithms automatically extracting relevant and descriptive features. The rapid evolution of these technologies has resulted in a profusion of strategies for converting natural language into machine-understandable data. This paper proposes the transfer learning approach, i.e., to utilize Bidirectional Encoder Representations from Transformers (BERT) and RoBERTa, pre-trained models for news article classification. The paper's focus is to fine-tune the hyperparameters of the pre-trained models to obtain a higher performance in classifying news articles. Pre-processing techniques like tokenization and extracting labels are performed before training the data on the models. The dataset utilized is an open-source dataset from Kaggle BBC news classification. Furthermore, the models are assessed using standard performance metrics like accuracy, cross-entropy loss, and time required for training. The results depict that both the models perform nearly the same by achieving a training accuracy of 100% and a validation accuracy of 99.1%. Nevertheless, the time required for training the RoBERTa model is more than the BERT model.
Zero-shot program representation learning

Learning program representations has been the core prerequisite of code intelligence tasks (e.g., code search and code clone detection). The state-of-the-art pre-trained models such as CodeBERT require the availability of large-scale code corpora. However, gathering training samples can be costly and infeasible for domain-specific languages such as Solidity for smart contracts. In this paper, we propose Zecoler, a zero-shot learning approach for code representations. Zecoler is built upon a pre-trained programming language model. In order to elicit knowledge from the pre-trained models efficiently, Zecoler casts the downstream tasks to the same form of pre-training tasks by inserting trainable prompts into the original input. Then, it employs the prompt learning technique to optimize the pre-trained model by merely adjusting the original input. This enables the representation model to efficiently fit the scarce task-specific data while reusing pre-trained knowledge. We evaluate Zecoler in three code intelligence tasks in two programming languages that have no training samples, namely, Solidity and Go, with model trained in corpora of common languages such as Java. Experimental results show that our approach significantly outperforms baseline models in both zero-shot and few-shot settings.
Hello, Mr Robot

As robots become ever more sophisticated, what future role could they play in the early years – and do practitioners need to fear for their jobs? Charlotte Goddard reports
The Use of Deep and Machine Learning for Face Expression Recognition: A Literature Review

In the current era, Artificial Intelligence (AI) and Computer Vision take the main role and participating to the people daily activities. Face Expression became as interesting topic to be explore. Face expression recognition or detection could be applied in many aspects, such as for student focus detection based on face expression, intruder detection system, lie detection and many more. Referring the useful of face expression detection, there are also much research that focused on the methodology to detects or recognize the face expression. Several approaches are use such by using Deep Learning, Machine Learning and statistical method. This paper focused on the process to obtain and knowing the best approach or methods to recognize the face expression by using Systematic Literature Review (SLR) process. From hundreds of retrieved papers with similar interest, we do the selection based on the interest, scope, and topic similarity. The process keeps repeating until we found papers that has similarity criteria in output, dataset, subject and the methods. There are 6 papers in total that fulfil our requirements based on subject, output, result and methods. Subsequently, every research paper was deep-reviewed and compared with another to find the best approaches proposed and dataset used.
DiffSearch: A Scalable and Precise Search Engine for Code Changes

The source code of successful projects is evolving all the time, resulting in hundreds of thousands of code changes stored in source code repositories. This wealth of data can be useful, e.g., to find changes similar to a planned code change or examples of recurring code improvements. This paper presents DiffSearch, a search engine that, given a query that describes a code change, returns a set of changes that match the query. The approach is enabled by three key contributions. First, we present a query language that extends the underlying programming language with wildcards and placeholders, providing an intuitive way of formulating queries that is easy to adapt to different programming languages. Second, to ensure scalability, the approach indexes code changes in a one-time preprocessing step, mapping them into a feature space, and then performs an efficient search in the feature space for each query. Third, to guarantee precision, i.e., that any returned code change indeed matches the given query, we present a tree-based matching algorithm that checks whether a query can be expanded to a concrete code change. We present implementations for Java, JavaScript, and Python, and show that the approach responds within seconds to queries across one million code changes, has a recall of 80.7% for Java, 89.6% for Python, and 90.4% for JavaScript, enables users to find relevant code changes more effectively than a regular expression-based search and GitHub's search feature, and is helpful for gathering a large-scale dataset of real-world bug fixes.
Training Object Detectors from Scratch: An Empirical Study in the Era of Vision Transformer

Modeling in computer vision has long been dominated by convolutional neural networks (CNNs). Recently, in light of the excellent performances of self-attention mech-anism in the language field, transformers tailored for visual data have drawn numerous attention and triumphed CNNs in various vision tasks. These vision transformers heavily rely on large-scale pre-training to achieve competitive accuracy, which not only hinders the freedom of architectural design in downstream tasks like object detection, but also causes learning bias and domain mismatch in the fine-tuning stages. To this end, we aim to get rid of the “pre-train & fine-tune” paradigm of vision transformer and train transformer based object detector from scratch. Some earlier work in the CNNs era have successfully trained CNNs based detectors without pre-training, unfortunately, their findings do not generalize well when the backbone is switched from CNNs to vision transformer. Instead of proposing a specific vision transformer based detector, in this work, our goal is to reveal the insights of training vision transformer based detectors from scratch. In particular, we expect those insights can help other re-searchers and practitioners, and inspire more interesting research in other fields, such as semantic segmentation, visual-linguistic pre-training, etc. One of the key findings is that both architectural changes and more epochs play critical roles in training vision transformer based detectors from scratch. Experiments on MS COCO datasets demonstrate that vision transformer based detectors trained from scratch can also achieve similar performances to their counterparts with ImageNet pre-training.