Reviewerly/exHarmony huggingface dataset:
Designing with Waste

This paper describes a series of collaborative studio explorations in examining waste. We assembled a design team to explore how designers might conceive, handle, and rework the material left behind as waste within an on-campus makerspace and adjacent design labs. We turned discarded 3D printing filament into a reparative glue for broken prints and dissolved cardboard boxes into a medium for pollinator habitats. We describe how attending to waste involves understanding the relationships that define it, like how a material comes to be categorized as biodegradable but is impossible to break down in practice. Bringing this insight to the design context, we introduce the tactic of ecological inversions, experiments in reversing material flows to expose the wider infrastructure on which they depend. We discuss how ecological inversions could invite design researchers to notice the infrastructural relationships that exceed the physical limitations of the makerspace, revealing challenges around complicity and legibility.
Using hierarchical statistical analysis and deep neural networks to detect covert timing channels

Covert timing channels provide a mechanism to leak data across different entities. Manipulating the timing between packet arrivals is a well-known example of such approach. The time based property makes the detection of the hidden messages impossible by traditional security protecting mechanisms such as proxies and firewalls. This paper introduces a new generic hierarchical-based model to detect covert timing channels. The detection process consists of the analysis of a set of statistical metrics at consecutive hierarchical levels of the inter-arrival times flows. The statistical metrics considered are: mean, median, standard deviation, entropy, Root of Average Mean Error (RAME). A real statistical metrics timing channel dataset of covert and overt channel instances is created. The generated dataset is set to be either flat where the statistical metrics are calculated on all flows of data or hierarchal (5 levels of hierarchy were considered) where the statistical metrics are computed on sub parts of the flow as well. Following this method, 5 different datasets were generated, and used to train/test a deep neural network based model. Performance results about accuracy and model training time showed that the hierarchical approach outperforms the flat one by 4 to 10 percent (in terms of accuracy) and was able to achieve short model training time (in terms of seconds). When compared to the Support Vector Machine (SVM) classifier, the deep neural network achieved a better accuracy level (about 2.3% to 12% depends on the used kernel) and significantly shorter model training time (few seconds versus few 100's of seconds). This paper also explores the importance of the used metrics in each level of the detection process.
Hand Gesture Recognition with Skin Detection and Deep Learning Method

Abstract Gesture recognition, although has been exploring for many years, is still a challenging problem. Complex background, camera angles and illumination conditions make the problem more difficult. Thus, this paper presents a fast and robust method for hand gesture recognition based on RGB video. First we detect the skin based on their color. Then we extract the contour and segment the hand region. Finally we recognize the gesture. The results of experiment demonstrate that the proposed method are efficient to recognize gesture with a higher accuracy than the state of the art.
Detecting the “gist” of breast cancer in mammograms three years before localized signs of cancer are visible

After a 500 ms presentation, experts can distinguish abnormal mammograms at above chance levels even when only the breast contralateral to the lesion is shown. Here, we show that this signal of abnormality is detectable 3 years before localized signs of cancer become visible.In 4 prospective studies, 59 expert observers from 3 groups viewed 116-200 bilateral mammograms for 500 ms each. Half of the images were prior exams acquired 3 years prior to onset of visible, actionable cancer and half were normal. Exp. 1D included cases having visible abnormalities. Observers rated likelihood of abnormality on a 0-100 scale and categorized breast density. Performance was measured using receiver operating characteristic analysis.In all three groups, observers could detect abnormal images at above chance levels 3 years prior to visible signs of breast cancer (p < 0.001). The results were not due to specific salient cases nor to breast density. Performance was correlated with expertise quantified by the number of mammographic cases read within a year. In Exp. 1D, with cases having visible actionable pathology included, the full group of readers failed to reliably detect abnormal priors; with the exception of a subgroup of the six most experienced observers.Imaging specialists can detect signals of abnormality in mammograms acquired years before lesions become visible. Detection may depend on expertise acquired by reading large numbers of cases.Global gist signal can serve as imaging risk factor with the potential to identify patients with elevated risk for developing cancer, resulting in improved early cancer diagnosis rates and improved prognosis for females with breast cancer.
A Geographical-Temporal Awareness Hierarchical Attention Network for Next Point-of-Interest Recommendation

Obtaining insight into user mobility for next point-of-interest (POI) recommendations is a vital yet challenging task in location-based social networking. Information is needed not only to estimate user preferences but to leverage sequence relationships from user check-ins. Existing approaches to understanding user mobility gloss over the check-in sequence, making it difficult to capture the subtle POI-POI connections and distinguish relevant check-ins from the irrelevant. We created a geographically-temporally awareness hierarchical attention network (GT-HAN) to resolve those issues. GT-HAN contains an extended attention network that uses a theory of geographical influence to simultaneously uncover the overall sequence dependence and the subtle POI-POI relationships. We show that the mining of subtle POI-POI relationships significantly improves the quality of next POI recommendations. A context-specific co-attention network was designed to learn changing user preferences by adaptively selecting relevant check-in activities from check-in histories, which enabled GT-HAN to distinguish degrees of user preference for different check-ins. Tests using two large-scale datasets (obtained from Foursquare and Gowalla) demonstrated the superiority of GT-HAN over existing approaches and achieved excellent results.
Binary Competitive Swarm Optimizer Approaches for Feature Selection

Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost.
Fixing of Security Vulnerabilities in Open Source Projects: A Case Study of Apache HTTP Server and Apache Tomcat

Software vulnerabilities are particularly dangerous bugs that may allow an attacker to violate the confidentiality, integrity or availability constraints of a software system. Fixing vulnerabilities soon is of primary importance; besides, it is crucial to release complete patches that do not leave any corner case not covered. In this paper we study the process of vulnerability fixing in Open Source Software. We focus on three dimensions: personal, i.e., who fixes software vulnerabilities; temporal, i.e., how long does it take to release a patch; procedural, i.e., what is the process followed to fix the vulnerability. In the context of our study we analyzed 337 CVE Entries regarding Apache HTTP Server and Apache Tomcat and we manually linked them to the patches written to fix such vulnerabilities and their related commits. The results show that developers who fix software vulnerabilities are much more experienced than the average. Furthermore, we observed that the vulnerabilities are fixed through more than a commit and, surprisingly, that in about 3% of the cases such vulnerabilities show up again in future releases (i.e., they are not actually fixed). In the light of such results, we derived some lessons learned that represent a starting point for future research directions aiming at better supporting developers during the documentation and fixing of vulnerabilities.
Psychophysiological Alteration After Virtual Reality Experiences Using Smartphone-Assisted Head Mount Displays: An EEG-Based Source Localization Study

Brain functional changes could be observed in people after an experience of virtual reality (VR). The present study investigated cyber sickness and changes of brain regional activity using electroencephalogram (EEG)-based source localization, before and after a VR experience involving a smartphone-assisted head mount display. Thirty participants (mean age = 25 years old) were recruited. All were physically healthy and had no ophthalmological diseases. Their corrected vision was better than 20/20. Resting state EEG and the simulator sickness questionnaire (SSQ) were measured before and after the VR experience. Source activity of each frequency band was calculated using the sLORETA program. After the VR experience, the SSQ total score and sub scores (nausea, oculomotor symptoms, and disorientation) were significantly increased, and brain source activations were significantly increased: alpha1 activity in the cuneus and alpha2 activity in the cuneus and posterior cingulate gyrus (PCG). The change of SSQ score (after–before) showed significant negative correlation with the change of PCG activation (after–before) in the alpha2 band. The study demonstrated increased cyber sickness and increased alpha band power in the cuneus and PCG after the VR experience. Reduced PCG activation in alpha band may be associated with the symptom severity of cyber sickness.
Balancing Prediction Errors for Robust Sentiment Classification

Sentiment classification is a popular text mining task in which textual content (e.g., a message) is assigned a polarity label (typically positive or negative) reflecting the sentiment expressed in it. Sentiment classification is used widely in applications like customer feedback analysis where robustness and correctness of results are critical. In this article, we highlight that prediction accuracy alone is not sufficient for assessing the performance of a sentiment classifier; it is also important that the classifier is not biased toward positive or negative polarity, thus distorting the distribution of positive and negative messages in the predictions. We propose a measure, called Polarity Bias Rate, for quantifying this bias in a sentiment classifier. Second, we present two methods for removing this bias in the predictions of unsupervised and supervised sentiment classifiers. Our first method, called Bias-Aware Thresholding (BAT), shifts the decision boundary to control the bias in the predictions. Motivated from cost-sensitive learning, BAT is easily applicable to both lexicon-based unsupervised and supervised classifiers. Our second method, called Balanced Logistic Regression (BLR) introduces a bias-remover constraint into the standard logistic regression model. BLR is an automatic bias-free supervised sentiment classifier. We evaluate our methods extensively on seven real-world datasets. The experiments involve two lexicon-based and two supervised sentiment classifiers and include evaluation on multiple train-test data sizes. The results show that bias is controlled effectively in predictions. Furthermore, prediction accuracy is also increased in many cases, thus enhancing the robustness of sentiment classification.
An Efficient Image Encryption Scheme Based on Signcryption Technique with Adaptive Elephant Herding Optimization

IoT makes incorporated communication circumstances of interconnected devices and stages by drawing in both practical and substantial worlds simultaneously. The researchers of the study distinguished and examined the vital open difficulties in fortifying the security in IoT that combines encryption strategies to offer security to exchanged images between connected networks of the two parties. The device is primarily based on a hybrid algorithm that applies the strategies of encryption and optimization techniques are used. This proposed image safety model signcryption with elephant based optimization method used. The purpose of the use of optimization in encryption method is to pick the most advantageous keys in encryption algorithms, here Adaptive Elephant Herding Optimization (AEHO) used. This technique Signcryption is the technique that mixes the functionality of encryption and digital signature in a single logical step. From the implementation, the results are evaluated through the usage of the Peak Signal to Noise Ratio (PSNR) and Mean square errors (MSE).
Forensic Inspection of Sensitive User Data and Artifacts from Smartwatch Wearable Devices

Wearable devices allow users the ability to leave mobile phones behind while remaining connected to the digital world; however, this creates challenges in the examination, acquisition, identification, and analysis of probative data. This preliminary research aims to provide an enhanced understanding of where sensitive user data and forensic artifacts are stored on smartwatch wearable devices, both through utilization as a connected and standalone device. It also provides a methodology for the forensically sound acquisition of data from a standalone smartwatch wearable device. The results identify significant amounts of data on the Samsung™ Gear S3 Frontier, greater than that stored on the companion mobile phone. An Apple Watch® Series 3 manual examination method which produces native screenshots was identified; however, the companion mobile phone was found to store the greatest amount of data. As a result of this research, a data extraction tool for the Samsung™ Gear S3 Frontier was created.
Combining Data Duplication and Graph Reordering to Accelerate Parallel Graph Processing

Performance of single-machine, shared memory graph processing is affected by expensive atomic updates and poor cache locality. Data duplication, a popular approach to eliminate atomic updates by creating thread-local copies of shared data, incurs extreme memory overheads due to the large sizes of typical input graphs. Even memory-efficient duplication strategies that exploit the power-law structure common to many graphs (by duplicating only the highly-connected "hub" vertices) suffer from overheads for having to dynamically identify the hub vertices. Degree Sorting, a popular graph reordering technique that re-assigns hub vertices consecutive IDs in a bid to improve spatial locality, is effective for single-threaded graph applications but suffers from increased false sharing in parallel executions. The main insight of this work is that the combination of data duplication and Degree Sorting eliminates the overheads of each optimization. Degree Sorting improves the efficiency of data duplication by assigning hub vertices consecutive IDs which enables easy identification of the hub vertices. Additionally, duplicating the hub vertex data eliminates false sharing in Degree Sorting since each thread updates its local copy of the hub vertex data. We evaluate this mutually-enabling combination of power-law-specific data duplication and Degree Sorting in a system called RADAR. RADAR improves performance by eliminating atomic updates for hub vertices and improving the cache locality of graph applications, providing speedups of up to 165x (1.88x on average) across different graph applications and input graphs.
Photometric Depth Super-Resolution

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shot strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.
Creating Persona Skeletons from Imbalanced Datasets - A Case Study using U.S. Older Adults' Health Data

Incorporating health personas for older adults into design processes can help designers accurately represent older adults by evoking empathy, facilitating consideration of health issues and needs, and reducing stereotype reliance. Toward this goal, we create a two-level quantitative methodology for constructing persona skeletons from imbalanced datasets. We demonstrate our methodology by constructing a set of 4 care-management personas for U.S. older adults via filtering and analyzing demographic, behavior risk factor, and chronic health conditions from 170,704 randomly sampled older adults in a national survey with imbalanced coverage (i.e. between unconditional & conditional questions). We obtain 4 cluster centers for unconditional questions through K-means and iteratively dropping irrelevant features. Within each cluster, we analyze selected respondents for conditional questions. We synthesize results into persona narratives and provide a weighting scheme to quantitatively measure each persona's significance. We contribute a robust persona construction methodology, here applied towards representing older adults.
Streaming 360° videos to head-mounted virtual reality using DASH over QUIC transport protocol

We design, implement, and evaluate a tiled DASH streaming system for 360° videos using QUIC/UDP protocol, in which multiplexed and prioritized streams are leveraged for sending urgent tiles that are about to miss their playout time. In particular, we develop a new architecture to concurrently request for regular tiled segments at a lower priority and urgent tiled segments at a higher priority as multiple streams over a single QUIC connection. Several core components, including the fixation prediction algorithm, fast tile selector, and Adaptive Bit Rate (ABR) controller are designed for this new architecture. Our trace-driven experiments reveal that: (i) DASH streaming over the QUIC protocol outperforms doing that over the HTTP/1.1 and HTTP/2 stacks and (ii) our urgent tiled segments reduce the missing ratio and increase the video quality without incurring excessive bandwidth utilization under diverse network bandwidth, user behavior, and video characteristics.
Driver Pose Estimation Using Recurrent Lightweight Network and Virtual Data Augmented Transfer Learning

Driver poses recognition contains three tasks such as body joint, head angle, and face landmark estimation, which is of paramount interest for the advanced driver assistance systems (ADAS). Recently proposed methods intend to use deeper and more complicated networks to achieve better performance, which leads to heavy models that are not feasible for the resource limited applications such as ADAS. To resolve this issue, we have worked on the following aspects: 1) a lightweight network model, which is referred to as recurrent multi-task thin net (RM-ThinNet), has been proposed which was especially designed for the computationally and memory limited devices; 2) a recurrent structure has been introduced to handle the scale difference and dependency between different tasks, and this recurrence ensures the different tasks are accomplished at different stages and their outputs can augment each other; and 3) a virtual data synthesization pipeline and a couple transfer learning method have been presented, by which network can be learnt effectively by relatively a small number of real data. Comparisons with the state-of-the-art methods reveal the superiority of the proposed method and competitive performance can be achieved with smaller model size and faster speed.
Distractor-Aware Visual Tracking by Online Siamese Network

The idea of most trackers based on Siamese network is off-line training and online tracking.In fact, online tracking is conducted in terms of deep features, which are extracted from the predefined network trained on a large amount of data off-line.However, these features are the general representation for similar objects, and therefore, their discrimination ability is not enough to identify the current tracking target, particularly distractors, from the background.To tackle this problem, we propose to update the features extracted by a Siamese network online.These features can fit the target variations when tracking is on-thefly.Especially, we extract the common features from the shallow convolutional layers trained off-line, and then, they are employed as inputs of the deep convolutional layers to learn the special features of the current target online.Besides, an integrated updating strategy is proposed to accelerate network convergence.It is beneficial to enhance the discrimination ability of the learned features to identify the current target from the background and distractors.We conducted abundant experiments on the OTB2015 and VOT2016 databases.And the results demonstrate that our tracker effectively improves the baseline algorithm and performs favorably against most of the state-of-the-art trackers in the comparison of accuracy and robustness.
An agile multi-frame detection method for targets with time-varying existence

Existing solutions for multi-frame detection (MFD) of dim targets commonly include a sliding window batch processing routine, in which multiple consecutive measurement frames are jointly processed at each measurement time. While the sliding window batch processing improves the detection performance, it can lead to serious performance deterioration. In particular, the batch processing approach is known to cause latencies in detection decisions once abrupt changes to target existence (such as target spawning or death) occur. The detection latency is fundamentally due to an implicit assumption in derivation of the test statistics: the status of target existence is assumed unchanged throughout the time horizon corresponding to the current sliding window. This paper presents a solution called Agile MFD that is immune to the performance degradation caused by unpredicted changes in target existence. Agile MFD is designed to discover a change in target existence rapidly by modeling target existence as a time-varying random variable, incorporating it in the test statistics, and estimating it along with the target states. The results of extensive numerical simulations demonstrate that the proposed Agile MFD method can significantly reduce the decision latency and achieve stable performance even in extreme cases where target existence randomly changes in frequent times.