Reviewerly/exHarmony huggingface dataset:
Clustering functional data using forward search based on functional spatial ranks with medical applications

Cluster analysis of functional data is finding increasing application in the field of medical research and statistics. Here we introduce a functional version of the forward search methodology for the purpose of functional data clustering. The proposed forward search algorithm is based on the functional spatial ranks and is a data-driven non-parametric method. It does not require any preprocessing functional data steps, nor does it require any dimension reduction before clustering. The Forward Search Based on Functional Spatial Rank (FSFSR) algorithm identifies the number of clusters in the curves and provides the basis for the accurate assignment of each curve to its cluster. We apply it to three simulated datasets and two real medical datasets, and compare it with six other standard methods. Based on both simulated and real data, the FSFSR algorithm identifies the correct number of clusters. Furthermore, when compared with six standard methods used for clustering and classification, it records the lowest misclassification rate. We conclude that the FSFSR algorithm has the potential to cluster and classify functional data.
A Smartphone-Based Virtual Reality Plotting System for STEM Education

We present a free student-facing tool for creating 3D plots and smartphone-based virtual reality (VR) visualizations for STEM courses. Visualizations are created through an in-browser interface using simple plotting commands. Then QR codes are generated, which can be interpreted with a free smartphone app, requiring only an inexpensive Google Cardboard compatible viewer. Example uses for math, physics, chemistry, and other STEM fields are presented, including a project-based assignment for calculus courses at The Ohio State University. The platform enables students, educators, and researchers to create stereoscopic VR visualizations on multiple platforms without needing to learn a complete VR development system.
Analysis of Social Engineering Attack on Cryptographic Algorithm

The primary objective of this research is to analyze the various types of social engineering attacks on cryptographic algorithm, the modus operandi of attackers, the damage that can be done, preventing measures, how to recover in the aftermath of such attacks and perhaps a new model of prevention against such attacks. The methodology for the analysis used in this research will be a case study method of research, done by thorough scrutiny of secondary data and the methodology used in the development of a new security model will be combination of various security algorithms to bring forth a new hybrid security model. The purpose of this study is to give an understanding regarding the matter of Social Engineering and to clarify how it may be utilized to damage a networks framework or/and trade off information and suggest a suitable model.
CubeMuseum: An Augmented Reality Prototype of Embodied Virtual Museum

An Augmented Reality (AR) prototype, CubeMuseum, is proposed in this paper to present an embodied experience with virtual museum collections. With a cost-effective cube and a smartphone application, users can view and interact with 3D museum objects embodied on the cube. Detailed design of the prototype is presented to illustrate the approaches to visualize, present, and interact with virtual objects. CubeMuseum has been evaluated by hundreds of users in both laboratory studies and public exhibitions. The results indicated that the prototype is simple yet effective. It demonstrates several benefits and potential implications in supporting user engagement and learning experience. This research provides insights to researchers and practitioners in designing interactive cultural heritage experiences using a cost-effective approach.
Detecting Compromised Edge Smart Cameras using Lightweight Environmental Fingerprint Consensus

Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment.
Formula-driven Supervised Learning with Recursive Tiling Patterns

Can convolutional neural networks pre-trained without natural images be used to assist natural image understanding? Formula-Driven Supervised Learning (FDSL) automatically generates image patterns and their category labels by assigning a well-organized formula. Due to the characteristics of not using natural images in pre-training phase, FDSL is expected to develop a trustworthy vision-based system in terms of human-annotation-free, fairer and more transparent datasets. In this paper, we propose TileDB which consists of recursive tiling patterns in the whole image and evaluates the family of FDSL such as the datasets consist of Perlin noise and Bezier curves. Experimental results show that our proposed TileDB pre-trained model performs much better than models trained from scratch, surpasses a similar self-supervised learning (SSL), and performs similarly to the models pre-trained with 100k-order natural image datasets such as ImageNet-100 and Places-30. By comparing to the FractalDB pre-trained model, the TileDB pre-trained model achieves better performances in a compact dataset (< 1,000 categories). Moreover, the image representation trained on TileDB can extract similar features to the ImageNet pre-trained model even though the training images are non-trivially different.
Designing a Multi-Modal Communication System for the Deaf and Hard-of-Hearing Users

In remote collaboration using Augmented Reality (AR), speech and gesture are major communication methods for the general public. However, the Deaf and Hard-of-Hearing (DHH) population cannot join in the communication due to the absence of a sign language interface which is their primary language. Recent works have tried to augment spoken language with sign language animations or captions, but the research to convey sign language with spoken language is still very limited. In this paper, we propose a novel multi-modal communication system that integrates sign language translation, speech recognition, and shared object manipulation in the mobile AR environment. Though the system is currently under development, we demonstrated a rapid prototype of the telemedicine app leveraging the video prototyping method to integrate the system modules. We performed preliminary interviews about our approach with DHH users, a sign language interpreter, and a physician. We discuss the insights into the future design of the DHH communication support in the AR collaboration system. This study has a socio-cultural, economic impact on the DHH population as a barrier-free design of a remote collaboration system in a practical scenario. Another contribution of this work is that we suggested a novel user-centered system for DHH users in AR by integrating the existing technologies.
IronyTR

Irony, which is a way of expression through the use of the opposite, commonly occurs in daily social media posts. Hence, automatic detection of irony is essential to understand the semantics of informal texts more accurately. The literature has several sentiment analysis studies on Turkish texts, but those focusing on irony detection are very few. This paper investigates the effectiveness of a rich set of supervised learning methods varying from traditional to deep neural solutions on Turkish texts. Traditional irony detection methods such as support vector machine (SVM) and tree-based binary classifiers are analyzed on Turkish informal texts. Furthermore, such methods are extended by polarity-based information and graph-based similarity scores as features. Additionally, neural architecture-based solutions including BERT and various LSTM network models are adapted for the problem. Irony detection performance of all the methods are comparatively analyzed on a data set collected within this study, which is larger than the previously used irony detection data sets in Turkish.
Geochemical characteristics of chlorite in the Luohe iron deposit in the middle-lower Yangtze metallogenic Belt, Eastern China

Chlorite is an alteration mineral commonly found in magmatic-hydrothermal deposits. In recent years, trace elements in chlorite have become an effective way to estimate the location of the mineralization centre (intrusive rocks) in many porphyry deposits, however, the usefulness of this approach has not yet been evaluated for iron oxide apatite (IOA) deposits. The Luohe iron deposit, located in the northwestern part of the Luzong (Lujiang-Zongyang) Basin of the Middle-Lower Yangtze Metallogenic Belt (MLYB), is the largest iron deposit in the MLYB. It is composed of a shallow (380–850 m) and a deep (1350–1800 m) ore body. However, no core intrusive rock has been found in the exploration area, which restricts field exploration and theoretical research of its metallogenic model. Based on the geological characteristics of the Luohe iron deposit, samples were collected from different locations along a 545 m long exploration tunnel and from four drillholes at depths ranging between 870 and 1850 m. The samples were analysed using in-situ laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to identify the trace element characteristics of chlorite. The results showed significant negative correlations of Fe vs Na, Fe vs Si, and Fe vs K, and a positive correlation of Fe vs Mg in chlorite. The ratios Ti/Pb, Mg/Sr, Ni/V, and Ti/Sr gradually increased from east to west, with the values highest in chlorite of drillhole zk2-1, and decreased northward and southward. The results confirm that the spatial distribution pattern of trace elements in chlorite in IOA deposits is similar to that in porphyry deposits. Therefore, a methodology similar to that for porphyry deposits was herein adopted for estimating the position of the mineralization centre in the Luohe iron deposit. According to the Ti/Sr ratios in Luohe chlorite, we estimate that the concealed mineralisation centre is located southwest of the lower ore body at a depth of approximately 2300–2500 m.
Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web

Modern web wants the data to be in Resource Description Framework (RDF) format, a machine-readable form that is easy to share and reuse data without human intervention. However, most of the information is still available in relational form. The existing conventional methods transform the data from RDB to RDF using instance-level mapping, which has not yielded the expected results because of poor mapping. Hence, in this paper, a novel schema-based RDB-RDF mapping method (relational database to Resource Description Framework) is proposed, which is an improvised version for transforming the relational database into the Resource Description Framework. It provides both data materialization and on-demand mapping. RDB-RDF reduces the data retrieval time for nonprimary key search by using schema-level mapping. The resultant mapped RDF graph presents the relational database in a conceptual schema and maintains the instance triples as data graph. This mechanism is known as data materialization, which suits well for the static dataset. To get the data in a dynamic environment, query translation (on-demand mapping) is best instead of whole data conversion. The proposed approach directly converts the SPARQL query into SQL query using the mapping descriptions available in the proposed system. The mapping description is the key component of this proposed system which is responsible for quick data retrieval and query translation. Join expression introduced in the proposed RDB-RDF mapping method efficiently handles all complex operations with primary and foreign keys. Experimental evaluation is done on the graphics designer database. It is observed from the result that the proposed schema-based RDB-RDF mapping method accomplishes more comprehensible mapping than conventional methods by dissolving structural and operational differences.
Preserving Contextual Privacy for Smart Home IoT Devices With Dynamic Traffic Shaping

Internet of Things (IoT) enables physical devices embedded with sensors, software, and other technologies to interoperate and exchange data with other systems over the Internet. Privacy is a huge concern for IoT devices as personal information is constantly being shared through them. Though the best industrial standards like end-to-end encryption are being followed to ensure content-based privacy, contextual privacy concerns still exist. This study focuses on user activity inference attacks, where a passive network observer can infer the private in-home activity of a user by analyzing encrypted IoT traffic metadata. Most of the previous solutions addressing these attacks have either reduced the usability of the devices, increased data overhead, or failed against packet-level signature-based attack scenarios. This study introduces a new defense mechanism that combines dummy packet generation with dynamic link padding. This process makes it difficult for the adversary to avail contextual information about the state of the device (ON or OFF), along with the temporal information (time of state change) from encrypted IoT traffic metadata. We reverse the packet-level signature-based attacks to get device-specific signatures, which helps us generate dummy traffic for the duration of device-specific signatures. Consequently, this results in increased false positives for device state identifications and low traffic overhead. We simulate a state-of-the-art attack scenario to test and vindicate our solution over existing data sets.
Exploring Algorithm Comprehension: Linking Proof and Program Code

An algorithm consists of the description of a process solving a well-defined problem and a chain of reasoning regarding one or more properties of this process, such as efficiency or correctness. Often, this chain of reasoning is presented as a formal proof. Understanding the links between this reasoning and the process is a critical task for understanding algorithm comprehension. In this article, we present results from a qualitative study in which we explored how and where links between parts of the process and parts of the chain of reasoning are established by a reader attempting to comprehend an algorithm. We relate our findings to research in program and proof comprehension and observe that the connection between process and reasoning introduces another dimension of comprehension not covered by previous work. We identify potential mechanisms in this comprehension process and derive suggestions for how to support comprehension when teaching algorithms.
Hydrogeochemical mineral exploration in deeply weathered terrains: An example from Mumbwa, Zambia

Locating mineral deposits in areas of thick or transported overburden is notoriously difficult. Post-mineral cover is prevalent in many parts of the globe and has led to prospective geological sequences being missed by traditional methods of exploration. Hydrogeochemistry is particularly applicable for the exploration of Iron Oxide Copper Gold (IOCG) deposits because, when compared to larger porphyry or sediment-hosted systems, IOCG deposits tend to be smaller and high-grade with a limited lateral footprint to intersect with grid-drilling; groundwater interactions and ion dispersion tend to produce a much larger anomaly target than regolith geochemistry alone and require fewer samples. As a case study, we examine the hydrogeochemistry of the Kitumba IOCG deposit, located in the Mumbwa district of west-central Zambia. We present physicochemical data (Eh, pH, TDS, conductivity), major and trace element concentrations, and isotopic compositions (δ98Mo, 87Sr/86Sr, and δ65Cu) from groundwaters interacting with the Kitumba deposit and surrounding prospects. A hydrogeochemical footprint of As, Mo, Fe, Mn, and Zn is dispersed from the deposit. Groundwater 87Sr/86Sr values (0.708832 to 0.731807) reflect the mixing in varying proportions of waters that have interacted with distinct lithological endmembers in the Mumbwa area, corresponding to a complicated tectonic and metamorphic history. We report fractionation of 1.34 to 1.60‰ (∆65Cugroundwater - chalcopyrite) between proximal groundwater and primary chalcopyrite, which we postulate may be related to the oxidative dissolution of primary sulfide minerals. The δ98Mo3134 values of groundwaters proximal to known ore bodies are isotopically distinct (-1.08 ± 0.18‰ 2SE to 0.64 ± 0.08‰ 2 SE) from background aquifers (2.08 ± 0.12‰ 2SE). The trace element and isotopic hydrogeochemical patterns described in this study document water-rock and water-deposit interactions and demonstrate the potential of non-traditional stable isotopes to be employed in district-scale reduction of exploration ground and vectoring towards undisturbed ore deposits similar to Kitumba.
New virtual machine placement approach based on the micro genetic algorithm in cloud computing

Cloud computing has become an essential part of modern digital transformation. It is ideal for organizations that need a dedicated system that gives them full control not only over their data but also over the hardware. Especially those running many types of applications and complex workloads. However, cloud resource allocation faces serious problems in terms of energy consumption and resource wastage. One of the resource allocation trends that improve the performance of cloud services is the search for virtual machine placement. It requires an appropriate strategy for optimal virtual machines deployment. In this context, this paper proposes a meta-heuristic approach called improved micro Genetic algorithm (IµLG A) for the placement of virtual machines. We specifically designed IµG A to minimize both energy consumption and resource wastage. The results of the simulation revealed that IµGA gave better results compared to the other methods.
When Massive GPU Parallelism Ain’t Enough: A Novel Hardware Architecture of 2D-LSTM Neural Network

Multidimensional Long Short-Term Memory (MD-LSTM) neural network is an extension of one-dimensional LSTM for data with more than one dimension. MD-LSTM achieves state-of-the-art results in various applications, including handwritten text recognition, medical imaging, and many more. However, its implementation suffers from the inherently sequential execution that tremendously slows down both training and inference compared to other neural networks. The main goal of the current research is to provide acceleration for inference of MD-LSTM. We advocate that Field-Programmable Gate Array (FPGA) is an alternative platform for deep learning that can offer a solution when the massive parallelism of GPUs does not provide the necessary performance required by the application. In this article, we present the first hardware architecture for MD-LSTM. We conduct a systematic exploration to analyze a tradeoff between precision and accuracy. We use a challenging dataset for semantic segmentation, namely historical document image binarization from the DIBCO 2017 contest and a well-known MNIST dataset for handwritten digit recognition. Based on our new architecture, we implement FPGA-based accelerators that outperform Nvidia Geforce RTX 2080 Ti with respect to throughput by up to 9.9 and Nvidia Jetson AGX Xavier with respect to energy efficiency by up to 48 . Our accelerators achieve higher throughput, energy efficiency, and resource efficiency than FPGA-based implementations of convolutional neural networks (CNNs) for semantic segmentation tasks. For the handwritten digit recognition task, our FPGA implementations provide higher accuracy and can be considered as a solution when accuracy is a priority. Furthermore, they outperform earlier FPGA implementations of one-dimensional LSTMs with respect to throughput, energy efficiency, and resource efficiency.
Two-Branch Asymmetric Model With Alternately Clustering for Unsupervised Person Re-Identification

In the field of unsupervised person Re-identification (Re-ID), mainstream methods adopt cluster algorithm to generate pseudo labels for training. Despite the effectiveness, the cluster algorithm generates noisy labels, which are retained in further model updating and hinder higher performance. To solve this problem, we propose a Two-branch Asymmetric Model with Alternately Clustering. Specifically, the designed Alternately Clustering (AC) strategy leverages a two-branch model to cluster different pseudo labels for each branch, which prevents the continuous existence of identical noisy labels. To establish a mapping between the two label sets, pseudo label mapping constraint (PLMC) module is devised, which helps retain reliable pseudo labels. Our method improves the quality of generated pseudo labels by keeping noisy labels changing and retaining the reliable ones. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods.
F-CAD: A Framework to Explore Hardware Accelerators for Codec Avatar Decoding

Creating virtual avatars with realistic rendering is one of the most essential and challenging tasks to provide highly immersive virtual reality (VR) experiences. It requires not only sophisticated deep neural network (DNN) based codec avatar decoders to ensure high visual quality and precise motion expression, but also efficient hardware accelerators to guarantee smooth real-time rendering using lightweight edge devices, like untethered VR headsets. Existing hardware accelerators, however, fail to deliver sufficient performance and efficiency targeting such decoders which consist of multi-branch DNNs and require demanding compute and memory resources. To address these problems, we propose an automation framework, called F-CAD (Facebook Codec avatar Accelerator Design), to explore and deliver optimized hardware accelerators for codec avatar decoding. Novel technologies include 1) a new accelerator architecture to efficiently handle multi-branch DNNs; 2) a multi-branch dynamic design space to enable fine-grained architecture configurations; and 3) an efficient architecture search for picking the optimized hardware design based on both application-specific demands and hardware resource constraints. To the best of our knowledge, F-CAD is the first automation tool that supports the whole design flow of hardware acceleration of codec avatar decoders, allowing joint optimization on decoder designs in popular machine learning frameworks and corresponding customized accelerator design with cycle-accurate evaluation. Results show that the accelerators generated by F-CAD can deliver up to 122.1 frames per second (FPS) and 91.6% hardware efficiency when running the latest codec avatar decoder. Compared to the state-of-the-art designs, F-CAD achieves 4.0× and 2.8× higher throughput, 62.5% and 21.2% higher efficiency than DNNBuilder [1] and HybridDNN [2] by targeting the same hardware device.