Reviewerly/exHarmony huggingface dataset:
Cybercrime Reporting Behaviors Among Small- and Medium-Sized Enterprises in the Netherlands

Despite the high prevalence of cybercrime victimization among businesses, only few of these crimes are reported to the police. This study used a sample of 529 Dutch small- and medium-sized enterprise (SME) owners, to examine which characteristics of the offence, the SME, and the SME owner predict cybercrime reporting behaviors. Moreover, the motives to either report cybercrime victimization to the police or not were examined. All respondents were shown three vignettes about fictional cybercrime incidents and were asked how they would react in this situation. Next, they were also asked about their reporting behaviors after actual cybercrime victimization. The large majority of SME owners said that they would report the incidents from the vignettes to the police, but after actual victimization only 14.1% of the cybercrimes was reported to the police. Seriousness and type of offense were the best predictors for cybercrime reporting, with cyber-enabled crimes being more often reported to the police than cyber-dependent crimes. Characteristics of the SME and the SME owner were often not related to reporting behaviors. Victims report cybercrime to the police because they want the perpetrator to be caught and to prevent him from doing the same to others, and they do not report cybercrimes because they think the police will not do anything and rather solve it themselves. When victims did report their victimization to the police, they were often unsatisfied because the police were indifferent and because the problems were not solved. Implications of these results for practice and future research are discussed.
Privacy-Preserving Travel Time Prediction With Uncertainty Using GPS Trace Data

The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of geo-indistinguishability-based sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time prediction with satisfactory accuracy at reasonably small privacy costs.
WaveFunctionCollapse: Content Generation via Constraint Solving and Machine Learning

In this article, we describe WaveFunctionCollapse (WFC), a new family of algorithms for content generation. WFC was recently invented by independent game developer M. Gumin and has since been adopted and adapted by other game developers. Trends in academic research on content generation have only recently suggested the use of ideas from constraint solving and machine learning, so it is surprising to see these manifested in in-the-wild algorithms developed outside of an academic context. We illuminate the common components in this family of algorithms by way of a rational reconstruction. Through experiments with the reconstruction we probe the impact of design choices made in various adaptations of WFC (e.g., the role of backtracking, search heuristics, or pattern classification and rendering strategies). This article highlights a mode of incremental content generation that has been overlooked by past surveys of content generation methods.
Smartwatch User Authentication Based on the Arm-Raising Gesture

Abstract Smartwatches have arguably become a popular wearable device nowadays. It is important to protect privacy data stored in smartwatches from being stolen. This study proposes a novel smartwatch user authentication technique based on the arm-raising gesture, which is the process of moving the arm from one side of the body to the chest height. We conducted two experiments to verify the effectiveness of the proposed technique. In Experiment 1, we investigated the performance of identifying users with the arm-raising gesture. We selected a set of features and applied them to five basic machine learning algorithms (i.e. random forest, simple logistic, naive Bayes, multilayer perceptron and linear classifier). Results with 32 participants show that with combined features, these classifiers generally achieved high authentication accuracy with high true accept rate (TAR) ($\geq $92.1% for random forest, simple logistic and multilayer perceptron), low false accept rate (FAR) ($\leq $0.6%) and large area under the curve (AUC) of receiver operating characteristics) ($\geq $92.4%). In Experiment 2, we examined the performance of identifying the arm-raising gesture across different day-to-day gestures. Results show that the arm-raising gesture can be identified from other eight common gestures with high TAR ($\geq $99.5%), low FAR ($\leq $3.6%) and large AUC ($\geq $99%). Overall, the results indicate that our technique could be a viable alternative for smartwatch user authentication.
The ownership challenge in the Internet of things world

The products of the fourth industrial revolution will come in the Internet of Things (IoT) architecture. They will be unified cyber-physical entities. A fallout of this paradigm is that our orthodox conceptions of private property and ownership will come under stress in ways not experienced before. The rights and boundaries of ownership will become obfuscated and hard to demarcate in the IoT-world. In this paper, we explore these issues from historical and theoretical perspectives to speculate on possible institutional and policy arrangements to manage the challenge. Towards this end, we propose a threshold utility principle of ownership that can serve as the starting point of addressing the private property and ownership challenge in the emergent IoT world.
Classification of Metastatic Breast Cancer Cell using Deep Learning Approach

Metastasis of cancer cells is one of the major reasons for cancer mortality, as an indicator of the stage of cancer. Since the degree of metastasis is often dependent upon the cancer cell mobility which is closely related to the shape of the cell, analyzing the cell shape is an essential factor to determine whether a cellular sample develops into a malignant tumor. In this paper, we attempted to classify two types of breast cancer cells based on their cell shapes, using a deep-learning approach.
Machine Learning Migration for Efficient Near-Data Processing

Machine Learning (ML) rises as a highly useful tool to analyze the vast amount of data generated in every field of science nowadays. Simultaneously, data movement inside computer systems gains more focus due to its high impact an time and energy consumption. In this context, the Near-Data Processing (NDP) architectures emerged as a prominent solution to increasing data by drastically reducing the required amount of data movement For NDP, we see three main approaches, Application-Specific Integrated Circuits (ASICs), full Central Processing Units (CPUs) and Graphics Processing Units (GPIIs), or vector units integration. However, previous work considered only ASICs, CPUs and GPUs when executing ML algorithms inside the memory. In this paper, we present an approach to execute ML algorithms near-data, using a general-purpose vector architecture and applying near-data parallelism to kernels from KNN, MEP, and CNN algorithms. To facilitate this process, we also present an NDP intrinsics library to ease the evaluation and debugging tasks. Our results show speedups up to fox for KNN, 11× for MLP, and 3× for convolution when processing near-data compared to a high-performance ×86 baseline.
LectYS: A System for Summarizing Lecture Videos on YouTube

Students leverage online resources such as online classes and YouTube is increasing. Still, there remain challenges for students to easily find the right lecture video online at the right time. Multiple video search methods have been proposed, but to our knowledge, no previous study has proposed a system that summarize YouTube lecture videos using subtitles. This demo proposes LectYS, a system for summarizing lecture videos on YouTube to support students search for lecture video content on YouTube. The key features of our proposed system are: (1) to summarize the lecture video using the subtitle of the video, (2) to access to the specific parts of the video using the start time of video subtitle, and (3) to search for the video with keyword. Using LectYS, students are allowed to search for lecture videos on YouTube faster and more accurately.
Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network

Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this study is to employ a linear-map convolutional neural network (CNN) to perform action recognition with RGB videos. To reduce the amount of the training data, the posture information is represented by skeleton data extracted from the 300 frames of one film. The two-stream method was applied to increase the accuracy of recognition by using the spatial and motion features of skeleton sequences. The relations of adjacent skeletal joints were employed to build the direct acyclic graph (DAG) matrices, source matrix, and target matrix. Two features were transferred by DAG matrices and expanded as color texture images. The linear-map CNN had a two-dimensional linear map at the beginning of each layer to adjust the number of channels. A two-dimensional CNN was used to recognize the actions. We applied the RGB videos from the action recognition datasets of the NTU RGB+D database, which was established by the Rapid-Rich Object Search Lab, to execute model training and performance evaluation. The experimental results show that the obtained precision, recall, specificity, F1-score, and accuracy were 86.9%, 86.1%, 99.9%, 86.3%, and 99.5%, respectively, in the cross-subject source, and 94.8%, 94.7%, 99.9%, 94.7%, and 99.9%, respectively, in the cross-view source. An important contribution of this work is that by using the skeleton sequences to produce the spatial and motion features and the DAG matrix to enhance the relation of adjacent skeletal joints, the computation speed was faster than the traditional schemes that utilize single frame image convolution. Therefore, this work exhibits the practical potential of real-life action recognition.
An aggregated template methodology: Novel automatic phase‐onset identification by template matching

ABSTRACT The precision of P‐ and S‐wave phase picking strongly determines the precision of earthquake locations, but such picking can be challenging in the case of emergent signals, large data sets or temporally varying seismic networks. To overcome these challenges, we have developed the concept of an aggregated template to perform automatic picking of the P‐ and S‐wave phases. An aggregated template is defined as a representative event for a small area, built by aggregating the best signal‐to‐noise‐ratio seismic traces from events with similar waveforms (i.e. multiplet events). A template matching procedure, based on the cross‐correlation between an aggregated template and an unpicked event, automatically determines the unpicked event P‐ and S‐wave phases. This method enables (1) consistent and accurate P‐ and S‐wave phase picking and (2) reduces processing time relative to traditional template matching by using a clustering method that finds the most representative templates for a region, and thus limiting the required number of templates. We established two parameters to weight the picking precision: (1) the cross‐correlation between the aggregated template and the unpicked event and (2) the number of P‐ and S‐wave picks determined per event. We tested this method on 2100 events recorded in the south‐west of Iceland. Nineteen aggregated templates have been defined and used to automatically pick ∼65% of the complete event catalogue with an accuracy within the range of the manual picking uncertainty. These automatically picked events can then be used for event location, even when characterized by low magnitude, low signal to noise ratios and with emergent P‐wave signals.
A Probe into Performance Analysis of Real-Time Forecasting of Endemic Infectious Diseases Using Machine Learning and Deep Learning Algorithms

The current work aims at probing the performance of real-time forecasting of endemic infectious diseases by means of machine learning and deep learning techniques. An LSTM-based time series forecasting framework and machine learning-based framework are proposed for forecasting the endemic infectious diseases in real time. With recent outbreaks of Ebola, Zika, cholera, and COVID 2019, a question is being raised on our alertness as well as preparedness toward controlling the spread of these pandemics. Accurate and reliable prediction occurrences of these diseases are compulsory for the health personals to enable timely response in handling these outbreaks. The diversities of the communities make it more complex along with the humongous data generated due to the convergence of SMAC technologies. The data generated due to this complex network is nonlinear and non-stationary. Processing of this data requires an effort from a multidimensional perspective. The current work proposed the utilization of machine learning and deep learning-based long short-term memory (LSTM) techniques for the assessment of time series forecasting of casualties in case of cholera outbreak that happened recently in Yemen. The feasibility of these two techniques is probed using performance evaluation metrics. The core objective of using these two techniques is in considering nonlinear and non-stationary behavior.
Improved ORB matching algorithm based on adaptive threshold

Abstract Aiming at the problem of ORB feature matching algorithm extracting background pixels as feature points and matching wrong feature points in a complex background environment, an improved ORB algorithm based on adaptive threshold is proposed, and GMS algorithm is used to screen out mismatches in the feature matching stage. First, the algorithm calculates the mean and standard deviation of the image to be matched and the reference image. Then it inputs the obtained data into the adaptive threshold calculation stage to obtain the adaptive threshold. Finally it inputs the adaptive threshold into the feature extraction and matching stage. The experimental results show that the improved ORB algorithm reduces the number of features extracted from the background of the exhibits in the complex environment of the museum, and the matching algorithm combined with the ORB algorithm and GMS increases the correct matching on the basis of slightly shorter time than the original algorithm. The algorithm has strong robustness and real-time performance.
New Properties of the Data Distillation Method When Working with Tabular Data

Data distillation is the problem of reducing the volume of training data while keeping only the necessary information. With this paper, we deeper explore the new data distillation algorithm, previously designed for image data. Our experiments with tabular data show that the model trained on distilled samples can outperform the model trained on the original dataset. One of the problems of the considered algorithm is that produced data has poor generalization on models with different hyperparameters. We show that using multiple architectures during distillation can help overcome this problem.
DIRAC: Dynamic-IRregulAr Clustering Algorithm with Incremental Learning for RF-Based Trust Augmentation in IoT Device Authentication

Unlike traditional radio frequency device authentication which utilizes security keys in conjunction with a digital subsystem for verification, human voice communication involves probabilistic identification of a person based on his/her voice signatures and improves the detection probability over time. Inspired by voice-based human identification, we implement a novel method of augmenting trust during device detection and authentication, involving dynamic irregular clustering which exploits the unique nonidealities in IoT devices as physical signatures originated from Radio Frequency (RF) circuitry. The proposed method increases the confidence level of the classification as more data come in from a particular device, and is also able to detect new devices that do not fall into any of the previous clusters. Using 30 Xbee modules as transmitters, we show that our proposed method can detect a transmitter with > 95% sensitivity 100% with optimum parameters) using only 0.2 milliseconds of test data which makes it suitable for a very low latency communication system. Also, the incremental learning feature of the proposed method renders a gradual increase in sensitivity as more data are available from the transmitter end. The proposed method can provide an additional security layer in conjunction with the existing methods without adding any additional burden, which is extremely important for resource-limited asymmetric IoT nodes.
Label Incorporated Graph Neural Networks for Text Classification

Graph Neural Networks (GNNs) have achieved great success on graph-structured data, and their applications on traditional data structures such as natural language processing and semi-supervised text classification have been extensively explored in recent years. While previous works only consider the text information while building the graph, heterogeneous information such as labels is ignored. In this paper, we consider incorporating the label information while building the graph by adding text-label-text paths, through which the supervision information will propagate among the graph more directly. Specifically, we treat labels as nodes in the graph which also contains text and word nodes, and then connect labels with texts belonging to that label. Through graph convolutions, label embeddings are jointly learned with text embeddings in the same latent semantic space. The newly incorporated label nodes will facilitate learning more accurate text embeddings by introducing the label information, and thus benefit the downstream text classification tasks. Extensive results on several benchmark datasets show that the proposed framework outperforms baseline methods by a significant margin.
ISVSF: Intelligent Vulnerability Detection Against Java via Sentence-Level Pattern Exploring

When software vulnerabilities threaten the security of users, new research on approaches to reduce security vulnerabilities must be explored. The development of deep learning has opened up the era of automatic code vulnerability detection, extricated humans from multifarious pattern definition, and feature selection. However, existing deep learning based vulnerability detection schemes are still in their early stage, most of them adopted token-level representing schemes, losing the logical information above token level and resulting in the narrowing of differences between codes. They always had low accuracy and high false positive rate. In addition, it is noticed that most code vulnerability detection methods focused on C/C++, and little work can be found on Java. In light of this, we propose an intelligent sentence-level vulnerability self-detection framework (ISVSF), which considers the syntax characteristics of Java and adopts sentence-level method representation and pattern exploration. Experimental results demonstrate that the ISVSF outperforms the existing token-level vulnerability detection schemes in terms of accuracy, false positive rate, detection time, etc. In addition, fast and strong vulnerability feature extraction enables ISVSF to learn vulnerability-related features quickly and achieve high accuracy with providing little training samples, thereby reducing the demand for training dataset effectively.
D-MmT: A concise decoder-only multi-modal transformer for abstractive summarization in videos

Multi-modal abstractive summarization for videos is an emerging task, aiming to integrate multi-modal and multi-source inputs (video, audio transcript) into a compressed textual summary. Although recent multi-encoder-decoder models on this task have shown promising performance, they did not explicitly model interactions of multi-source inputs. While some strategies like co-attention are utilized for modeling this interaction, considering ultra-long sequences and additional decoder in this task, the coupling of multi-modal data from multi-encoders and decoder needs complicated structure and additional parameters. In this paper, we propose a concise Decoder-only Multi-modal Transformer (D-MmT) based on the above observations. Specifically, we cut the encoder structure, and introduce an in-out shared multi-modal decoder to make the multi-source and target fully interact and couple in the shared feature space, reducing the model parameter redundancy. Also, we design a concise cascaded cross-modal interaction (CXMI) module in the multi-modal decoder that generates joint fusion representations and spontaneously establishes a fine-grained intra- and inter- association between multi-modalities. In addition, to make full use of the ultra-long sequence information, we introduce a joint in-out loss to make the input transcript also participate in backpropagation to enhance the contextual feature representation. The experimental results on the How2 dataset show that the proposed model outperforms the current state-of-the-art approach with fewer model parameters. Further analysis and visualization show the effectiveness of our proposed framework.
Indoor Distance Measurement System COPS (COVID-19 Prevention System)

With the rapid spread of coronavirus disease 2019 (COVID-19), measures are needed to detect social distancing and prevent further infection. In this paper, we propose a system that detects social distancing in indoor environments and identifies the movement path and contact objects according to the presence or absence of an infected person. This system detects objects through frames of video data collected from a closed-circuit television using You Only Look Once (v. 4) and assigns and tracks object IDs using DeepSORT, a multiple object tracking algorithm. Next, the coordinates of the detected object are transformed by image warping the area designated by the top angle composition in the original frame. The converted coordinates are matched with the actual map to measure the distance between objects and detect the social distance. If an infected person is present, the object that violates the movement path and social distancing of the infected person is detected using the ID assigned to each object. The proposed system can be used to prevent the rapid spread of infection by detecting social distancing and detecting and tracking objects according to the presence of infected persons.