Reviewerly/exHarmony huggingface dataset:
Pruning-Aware Merging for Efficient Multitask Inference

Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost. Pruning each network separately yields suboptimal computation cost due to task relatedness. A promising remedy is to merge the networks into a multitask network to eliminate redundancy across tasks before network pruning. However, pruning a multitask network combined by existing network merging schemes cannot minimise the computation cost of every task combination because they do not consider such a future pruning. To this end, we theoretically identify the conditions such that pruning a multitask network minimises the computation of all task combinations. On this basis, we propose Pruning-Aware Merging (PAM), a heuristic network merging scheme to construct a multitask network that approximates these conditions. The merged network is then ready to be further pruned by existing network pruning methods. Evaluations with different pruning schemes, datasets, and network architectures show that PAM achieves up to 4.87x less computation against the baseline without network merging, and up to 2.01x less computation against the baseline with a state-of-the-art network merging scheme.
A STUDY ON USER ACCEPTANCE OVER THE USAGE OF PLUSTRAFIK TWITTER

This study inten ds to measure the acceptance level of Twitter usage as a news media which deployed by PLUSTrafik among the PLUSTrafik Twitter users.  PLUSTrafik Twitter service is one of the PLUS Expressway Berhad’s continuous efforts to deliver information on latest traffic conditions along their expressway. Despite the ability to deliver data or information to interested users over multiple delivery channels, Twitter is associated with unsubstantiated rumors, gossip and wild inaccuracies. Veracity and validity of messages delivered were always questioned. Technology Acceptance Model by Davis (2016) was adopted at preparing the framework of this study. Perceived ease of use and perceived usefulness were the two important variables in this model.  Level of acceptance of the new service is dependent on perceived ease of use and perceived usefulness. This study applies a quantitative methodology approach by using an online survey method.  The data were collected from a sample of 318 PLUSTrafik Twitter followers using a convenience sampling procedure. In this study, we identify that both variables have a high perceived level by the users. The relationship between perceived ease of use, perceived usefulness towards the level of acceptance were tested using Pearson Product Moment correlation analysis.  Findings of the study indicated that perceived ease of use and perceived usefulness are strong determinants towards the level of acceptance to PLUSTrafik Twitter.  The relationship between the independent variables and dependent variables are significant base d on Pearson correlation test. As a conclusion, PLUSTrafik Twitter is highly accepted as a news media by its user.
Yemeni Paper Currency Recognition System Using Deep Learning Approach

The recognition of paper currency with various designs is a difficult task for automated banking machines and people with low visions. Due to the wide use of automated system such as ATM, for getting cash, safe and precise methods for recognizing paper currency are highly demanded. Recently, the use of an efficient automatic classifying system becomes one of the most important needs of current banking services. The main aim of this paper is to introduce an intelligent model that can distinguish between different types of Yemeni paper currencies using convolution neural networks (CNN). The system is based on image processing methods to classify different types of currencies efficiently. Moreover, deep learning techniques are used to perform the classification process effectively. The obtained results in the actual implementation of this model are encouraging. The accuracy rate reaches 92.7% in the testing phases.
Capturing Implicit Spatial Cues for Monocular 3d Hand Reconstruction

With the development of the parameterized hand model (e.g. MANO), it is possible to reconstruct the 3D hand mesh from a single 2D hand image by learning a few hand model parameters, rather than estimating hundreds of vertices on the mesh. However, it is highly non-linear to learn these parameters from the 2D hand image, as there is no explicit spatial correspondence between these parameters and image pixels. In this paper, we successfully leverage the graph convolutional network (GCN) to capture implicit spatial cues for fitting the well-known MANO hand model, thus greatly improving the performance of monocular 3D hand reconstruction. Our proposed MANO-GCN establishes the spatial hand mesh and hand joints graph for learning MANO parameters, with node features propagated along edges to utilize the spatial information. Among all monocular 3D hand reconstruction methods with MANO hand model, MANO-GCN achieves state-of-the-art accuracy on public FreiHAND and HO-3D benchmarks, without any bells and whistles. Code is available at https://github.com/ChenJoya/manogcn.
Multibyte Microarchitectural Data Sampling and its Application to Session Key Extraction Attacks

Microarchitectural data sampling (MDS) attacks leak secret data from the internal buffers of a processor to the attacker during transient execution.Because of the narrow window of transient execution, previous MDS attacks relied on repetitive sampling to obtain arbitrarily sized data from the buffer.However, as an MDS attacker cannot control the address for data leakage, such an approach significantly degrades the signal-to-noise ratio in the sampled data.In this paper, we propose a novel multibyte microarchitectural data sampling technique for performing MDS attacks.The proposed technique allows several continuous bytes to be captured in one execution without repetition of sampling.The implementation of the technique is quite challenging, because a transient execution window is not sufficiently large to allow multibyte sampling to be completed.We address this problem by leveraging a return stack buffer-based speculation technique, which originally was used for variants of Spectre-type attacks.We repurpose it to enlarge the transient execution window in our attack.Our implementations can capture data of up to 16 bytes in length in one execution from a line-fill buffer.To validate the effectiveness of the multibyte sampling technique, we demonstrate session key extraction attacks against secure network protocols.In particular, our objective is to extract AES-128 and AES-256 keys from TLS and SSH applications.To recover session keys in a postprocessing phase efficiently, we also propose a novel clustering-based search method that assembles the bytes of interest from the noisy sampled data.The experimental results show that our technique can successfully extract AES-128/256 session keys from victim applications with a probability of at least 98% and a reasonable search complexity.
USING THE MINECRAFT: EDUCATION EDITION PLATFORM IN INFORMATICS LESSONS

The article discusses the capabilities of the Minecraft: Education Edition (Minecraft EDU) platform, built on the ideas of gamification, to motivate schoolchildren to study informatics. It describes how to work with the platform (creating an account, importing worlds, creating programs); basic concepts associated with the platform; approximate planning of lessons of the thematic section "Algorithmics" for the sixth grade; general idea of work in the classroom using the platform. The materials of two lessons are presented in detail: the first is focused on acquainting students with Minecraft EDU, the basic rules and tools of this platform; the second is focused on compose linear programs, acquaintance with such concepts of informatics as "algorithm", "executor", "system of commands of the executor". For each of the two lessons, the game world, specially prepared by the teacher, is described in detail. The results obtained by the author when testing these materials are formulated.
Keynote: Monitoring People and their Vital Signs Using Radio Signals and Machine Learning

Summary form only given, as follows. A complete record of the panel discussion was not made available for publication as part of the conference proceedings. In this talk, I will present sensing technologies that track people&#x2019;s gait and movements based purely on the radio signals that bounce off their bodies. They can further monitor a person&#x2019;s breathing, heartbeats, and sleep quality remotely, without requiring any physical contact with the human body. They operate by transmitting a low-power wireless signal and analyzing its reflections using machine learning models. We show results from using these sensors for remote health monitoring of patients with Parkinson&#x2019;s, Alzheimer&#x2019;s, and COVID-19. We envision that such technologies can enable truly smart homes that learn people&#x2019;s habits and passively monitor their vital signs to allow for early detection of health problems and improve overall health and well-being.
The Ornstein-Uhlenbeck Process in Multi-Target Tracking

This paper introduces a multiple-model Ornstein-Uhlenbeck (MM-OU) formulation of target motion uncertainty. This model exploits a weighted set of learned motion trajectories for targets. Each realization of target motion is governed by one trajectory in the set, with deviations according to a stable 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order Ornstein-Uhlenbeck process. Based on this target model, we develop a (non-interacting) MM-OU filter that maintains a weighted set of filter solutions. This filter is useful in setting with nontrivial sensor coverage gaps. We have integrated the MM-OU filter as part of an advanced multiple-hypothesis tracking solution to the multi-target tracking problem. We examine the benefits of the new filter compared to baseline processing that adopts the classical nearly-constant velocity filter, with both single-target and multi-target scenarios.
Working with Few Samples: Methods that Help Analyze Social Attitude and Personal Emotion

In the past decade, sentiment analysis on social media has attracted great attention and has been used in many studies in CSCW and related fields. Recently, with the rapid development of machine learning, using machine learning methods to analyze sentiment has become an efficient experiment framework. Now, the existing sentiment analysis methods in machine learning are mainly based on supervised learning, and they need enough training data to ensure high accuracy. They encounter a common problem that they cannot recognize and calculate the emotion of samples with unseen labels, which don't belong to the training set. However, most data collected from social media is unstructured and unlabeled, which challenges the effectiveness and usability of existing methods. In our study, we first refer to existing sentiment analysis methods and zero-shot learning for addressing the problem. After that, we propose two zero-shot sentiment analysis methods and design an experiment to compare our methods and strong baselines. In conclusion, our methods obtain better results and we try to apply these methods to future social media research.
A Crowdsourcing-Based Knowledge Graph Construction Platform

Nowadays, knowledge graphs are backbones of many information systems that require to have access to structured knowledge. While there are many openly available knowledge graphs, self-constructed knowledge graphs in specific domains are still in need, and the process of construction usually consumes a lot of manpower. In this paper, we present a novel platform that takes advantage of crowdsourcing to construct and manage knowledge graphs. The platform aims to provide knowledge graph automatic construction as a service and reduce the tenants’ effort to construct knowledge graphs.
Leveraging Text Classification by Co-training with Bidirectional Language Models – A Novel Hybrid Approach and Its Application for a German Bank

Labeling training data constitutes the largest bottleneck for machine learning projects. In particular, text classification via machine learning is widely applied and investigated. Hence, companies have to label a decent amount of texts manually in order to build appropriate text classifiers. Obviously, labeling texts manually is associated with time and expenses. Against this background, research started to develop approaches exploiting the knowledge contained in unlabeled texts by learning sophisticated text representations or labeling some of the texts in an automated manner. However, there is still a lack of integrated approaches, considering both types of approaches to further reduce time and expenses for labeling texts. To address this problem, we propose a new hybrid text classification approach combining recent text representations and automated labeling approaches in an integrated perspective. We demonstrate and evaluate our approach using the case of a German bank where the approach could be applied successfully.
Non-stationary content-adaptive projector resolution enhancement

Digital imagery and video can have content at higher resolutions than can be projected by most data projectors, which has led to a variety of techniques to improve the high-resolution perception from lower-resolution displays. However, the downsampling procedures frequently used to fit an original image or video of high-resolution into a lower-resolution projector cause a frustrating loss of fine structures in the projected imagery. Since the human visual system is more sensitive to certain image phenomena, such as text and edges, an optimal approach to preserving fine structures should further sharpen such displayed content. On the other hand, the human visual system is also very sensitive to aliasing effects in motion, such that over-sharpening can lead to significant motion artifacts. In this paper, a new non-stationary content-adaptive resolution enhancement scheme is proposed. Our main objective in this study is to reduce the severity of artifacts due to the image enhancement processes. To achieve this goal, distribution-based text detection and hypothesis-testing-based motion detection methods are developed. Three spatial kernels, each constructed using a new band-limited Wiener deconvolution filter, are used to enhance a given image with different sharpening strengths, where the differently enhanced images are combined using a weighted non-stationary filter. For evaluation, a new visual projection assessment (VPA) dataset along with new metrics for quantifying motion artifacts are introduced. Experimental results show that the proposed non-stationary content-adaptive resolution enhancement scheme offers improved visual quality over the state-of-the-art while offering a reasonable balance between high text sharpness and reduced motion artifacts.
Securing Healthcare Records using Blockchain Technology

The modern day methods used to record as well as share patient related data are deeply flawed. These shortcomings limit the amount of access patients have to their clinical records, reduce availability of essential data to healthcare providers, and thus stand as an obstacle in creating the ideal health care system. Storing crucial patient related data on a Blockchain can help overcome the shortcomings posed by the current model. Whenever you read about blockchain, its application in banking or financial sector comes to our mind. This paper presents a comprehensive overview on blockchain technology. We provide an overview of blockchain architechture, security in blockchain & its advantages. Furthermore, it explores its application in a novel sector, i.e. healthcare sector. The paper discusses how healthcare records can be secured, using blockchain technology, in a health information system.
Personalized Lecture Recommendations to Facilitate Bite-Sized Learning

This paper presents an unsupervised, content-based approach to match users with shorter pieces of specific learning content, lectures, to target their learning goals at a more granular level. This method is especially useful when implicit data is unreliable or limited. At a high level, our approach generates a set of lectures for every topic via clustering and then matches lectures to users via users' topic affinities. Our central hypothesis is that important, fundamental concepts are repeated within many courses on the same topic, and by extracting clusters, we can identify these key information lectures per topic.
Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements

High-fidelity Graphical User Interface (GUI) prototyping represents a suitable approach for allowing to clarify and refine requirements elicitated from customers. In particular, GUI prototypes can facilitate to mitigate and reduce misunderstandings between customers and developers, which may occur due to the ambiguity and vagueness of informal Natural Language (NL). However, employing high-fidelity GUI prototypes is more time-consuming and expensive compared to other simpler GUI prototyping methods. In this work, we propose a system that automatically processes Natural Language Requirements (NLR) and retrieves fitting GUI prototypes from a semi-automatically created large-scale GUI repository for mobile applications. We extract several text segments from the GUI hierarchy data to obtain textual representations for the GUIs. To achieve ad-hoc GUI retrieval from NLR, we adopt multiple Information Retrieval (IR) approaches and Automatic Query Expansion (AQE) techniques. We provide an extensive and systematic evaluation of the applied IR and AQE approaches for their effectiveness in terms of GUI retrieval relevance on a manually annotated dataset of NLR in the form of search queries and User Stories (US). We found that our GUI retrieval performs well in the conducted experiments and discuss the results.
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection

The use of deep features coming from pre-trained neural networks for unsupervised anomaly detection purposes has recently gathered momentum in the computer vision field. In particular, industrial inspection applications can take advantage of such features, as demonstrated by the multiple successes of related methods on the MVTec Anomaly Detection (MVTec AD) dataset. These methods make use of neural networks pre-trained on auxiliary classification tasks such as ImageNet. However, to our knowledge, no comparative study of robustness to the low data regimes between these approaches has been conducted yet. For quality inspection applications, the handling of limited sample sizes may be crucial as large quantities of images are not available for small series. In this work, we aim to compare three approaches based on deep pre-trained features when varying the quantity of available data in MVTec AD: KNN, Mahalanobis, and PaDiM. We show that although these methods are mostly robust to small sample sizes, they still can benefit greatly from using data augmentation in the original image space, which allows to deal with very small production runs.
A Joint Embedding Technique for Sequential Recommendation

From many e-commerce websites such as Amazon, Flipkart and Netflix to online advertisements, recommender systems are used to recommend certain products to their users depending on their preferences and interactions with the products. Sequential recommender system is useful to model the short term behavior of the users depending on their latest interactions. Many techniques ranging from Markov chain models to convolutional neural networks (CNNs) have been used to solve the sequential recommendation problem. The most efficient state-of-the-art-model that uses CNNs is the top-N sequential recommendation system [1]. This model fails to capture the skip behavior in the union-level patterns as it doesn't take the interaction between distant items in a sequence into consideration. This paper adopts the basic architecture proposed in Caser [1] and introduction of the joint embedding of two items, formation of a 3D tensor with all the embeddings and a introduction of a new convolutional block have been made. This novel approach improves the mean average precision (MAP) from 0.1507 to 0.1884 which is significant and gives rise to the most efficient sequential recommendation system that uses CNN.
Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems

A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously utilized Expectation-Maximization (EM) approach, requiring setting the number of mixtures arbitrarily as an input parameter, was extended with the algorithm that automatically adapts the model to statistical data. The probabilistic model was obtained by matching to the histogram of transition times between a particular pair of cameras. The proposed matching procedure uses a modified particle swarm optimization (mPSO). A way of using models of transition time in object re-identification is also presented. Experiments with the proposed method of modeling the transition time were carried out, and a comparison between previous and novel approach results are also presented, revealing that added swarms approximate normalized histograms very effectively. Moreover, the proposed swarm-based algorithm allows for modelling the same statistical data with a lower number of summands in GMM.
Processing-based interactive installation art design—An example of a potentially hazardous odour identification device for the vision-impaired

Abstract Nowadays, as cutting-edge technologies such as information technology and artificial intelligence continue to develop at a rapid pace, the integration of technology and art is being promoted, which has led to an unprecedented development of interactive installation art. Processing, as one of the open-source programming languages, plays an important role in the applications of the creation of dynamic visual art and interactive media art. This paper examines the application of Processing in interactive installation art, using the example of a potentially hazardous odour identification device for the vision-impaired, and explores the significance of programming techniques in interactive installation art, offering new ideas for the future direction of interactive installation art creation.