Reviewerly/exHarmony huggingface dataset:
Achieving adversarial robustness via sparsity

Network pruning has been known to produce compact models without much accuracy degradation. However, how the pruning process affects a network's robustness and the working mechanism behind remain unresolved. In this work, we theoretically prove that the sparsity of network weights is closely associated with model robustness. Through experiments on a variety of adversarial pruning methods, we find that weights sparsity will not hurt but improve robustness, where both weights inheritance from the lottery ticket and adversarial training improve model robustness in network pruning. Based on these findings, we propose a novel adversarial training method called inverse weights inheritance, which imposes sparse weights distribution on a large network by inheriting weights from a small network, thereby improving the robustness of the large network.
Adaptive Computation Reuse for Energy-Efficient Training of Deep Neural Networks

In recent years, Deep Neural Networks (DNNs) have been deployed into a diverse set of applications from voice recognition to scene generation mostly due to their high-accuracy. DNNs are known to be computationally intensive applications, requiring a significant power budget. There have been a large number of investigations into energy-efficiency of DNNs. However, most of them primarily focused on inference while training of DNNs has received little attention. This work proposes an adaptive technique to identify and avoid redundant computations during the training of DNNs. Elements of activations exhibit a high degree of similarity, causing inputs and outputs of layers of neural networks to perform redundant computations. Based on this observation, we propose Adaptive Computation Reuse for Tensor Cores (ACRTC) where results of previous arithmetic operations are used to avoid redundant computations. ACRTC is an architectural technique, which enables accelerators to take advantage of similarity in input operands and speedup the training process while also increasing energy-efficiency. ACRTC dynamically adjusts the strength of computation reuse based on the tolerance of precision relaxation in different training phases. Over a wide range of neural network topologies, ACRTC accelerates training by 33% and saves energy by 32% with negligible impact on accuracy.
Global and local information aggregation network for edge-aware salient object detection

Aggregation of local and global contextual information by exploiting multi-level features in a fully convolutional network is a challenge for the pixel-wise salient object detection task. Most existing methods still suffer from inaccurate salient regions and blurry boundaries. In this paper, we propose a novel edge-aware global and local information aggregation network (GLNet) to fully exploit the integration of side-output local features and global contextual information and utilization of contour information of salient objects. The global guidance module (GGM) is proposed to learn discriminative multi-level information with the direct guidance of global semantic knowledge for more accurate saliency prediction. Specifically, the GGM consists of two key components, where the global feature discrimination module exploits the inter-channel relationship of global semantic features to boost representation power, and the local feature discrimination module enables different side-output local features to selectively learn informative locations by fusing with global attentive features. Besides, we propose an edge-aware aggregation module (EAM) to employ the correlation between salient edge information and salient object information for generating estimated saliency maps with explicit boundaries. We evaluate our proposed GLNet on six widely-used saliency detection benchmark datasets by comparing with 17 state-of-the-art methods. Experimental results show the effectiveness and superiority of our proposed method on all the six benchmark datasets.
A Melody-Unsupervision Model for Singing Voice Synthesis

Recent studies in singing voice synthesis have achieved high-quality results leveraging advances in text-to-speech models based on deep neural networks. One of the main issues in training singing voice synthesis models is that they require melody and lyric labels to be temporally aligned with audio data. The temporal alignment is a time-exhausting manual work in preparing for the training data. To address the issue, we propose a melody-unsupervision model that requires only audio-and-lyrics pairs without temporal alignment in training time but generates singing voice audio given a melody and lyrics input in inference time. The proposed model is composed of a phoneme classifier and a singing voice generator jointly trained in an end-to-end manner. The model can be fine-tuned by adjusting the amount of supervision with temporally aligned melody labels. Through experiments in melody-unsupervision and semi-supervision settings, we compare the audio quality of synthesized singing voice. We also show that the proposed model is capable of being trained with speech audio and text labels but can generate singing voice in inference time.
Data integrity verification using HDFS framework in data flow material environment using cloud computing

A distributed computing platform is preferred to meet out the current challenges of cloud computing paradigm. Hadoop manages distribution of large volume of big data and data is stored using cluster analysis of data. Hadoop Distributed File System (HDFS) is used to manage the problems in handling big data. HDFS can easily store large terabytes of data using commodity servers. The proposed system provides efficient data integrity verification service for big data management based on HDFS. HDFS has data integrity commitment, since it handles large volumes of data. Hadoop HDFS framework uses CRC-32C checksum verification on data node of content blocks to identify data corruption. A data aware module is implemented in Hadoop which provides more clustering process and reduces the computing performances of server. A broad range of data types is analyzed and clustered. The proposed research work uses balanced and proxy encryption technique using Cloud me tool and gives optimized query time and resource usage.
Just Speak It: Minimize Cognitive Load for Eyes-Free Text Editing with a Smart Voice Assistant

Entering text precisely by voice, users might encounter colloquial inserts, inappropriate wording, and recognition errors, which brings difficulties to voice editing. Users need to locate the errors and then correct them. In eyes-free scenarios, this select-modify mode brings a cognitive burden and a risk of error. This paper introduces neural networks and pre-trained models to understand users' revision intention based on semantics, reducing the need for the information from users' statements. We present two strategies. One is to remove the colloquial inserts automatically. The other is to allow users to edit by just speaking out the target words without having to say the context and the incorrect text. Accordingly, our approach can predict whether to insert or replace, the incorrect text to replace, and the position to insert. We implement these strategies in SmartEdit, an eyes-free voice input agent controlled with earphone buttons. The evaluation shows that our techniques reduce the cognitive load and decrease the average failure rate by 54.1% compared to descriptive command or re-speaking.
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling

Heterogeneous information network (HIN) embedding is an important tool for tasks such as node classification, community detection, and recommendation. It aims to find the representations of nodes that preserve the proximity between entities of different nature. A family of approaches that are widely adopted applies random walk to generate a sequence of heterogeneous contexts, from which, the embedding is learned. However, due to the multipartite graph structure of HIN, hub nodes tend to be over-represented to their context in the sampled sequence, giving rise to imbalanced samples of the network. Here, we propose a new embedding method: CoarSAS2hvec. The self-avoiding short sequence sampling with the HIN coarsening procedure (CoarSAS) is utilized to better collect the rich information in HIN. An optimized loss function is used to improve the performance of the HIN structure embedding. CoarSAS2hvec outperforms nine other methods in node classification and community detection on four real-world data sets. Using entropy as a measure of the amount of information, we confirm that CoarSAS catches richer information of the network compared with that through other methods. Hence, the traditional loss function applied to samples by CoarSAS can also yield improved results. Our work addresses a limitation of the random-walk-based HIN embedding that has not been emphasized before, which can shed light on a range of problems in HIN analyses.
Enhancing human-machine teaming for medical prognosis through neural ordinary differential equations (NODEs)

Machine learning (ML) has recently been demonstrated to rival expert-level human accuracy in prediction and detection tasks in a variety of domains, including medicine. Despite these impressive findings, however, a key barrier to the full realization of ML’s potential in medical prognoses is technology acceptance. Recent efforts to produce explainable AI (XAI) have made progress in improving the interpretability of some ML models, but these efforts suffer from limitations intrinsic to their design: they work best at identifying why a system fails, but do poorly at explaining when and why a model’s prediction is correct. We posit that the acceptability of ML predictions in expert domains is limited by two key factors: the machine’s horizon of prediction that extends beyond human capability, and the inability for machine predictions to incorporate human intuition into their models. We propose the use of a novel ML architecture, Neural Ordinary Differential Equations (NODEs) to enhance human understanding and encourage acceptability. Our approach prioritizes human cognitive intuition at the center of the algorithm design, and offers a distribution of predictions rather than single outputs. We explain how this approach may significantly improve human-machine collaboration in prediction tasks in expert domains such as medical prognoses. We propose a model and demonstrate, by expanding a concrete example from the literature, how our model advances the vision of future hybrid human-AI systems.
Camera-Agnostic Person Re-Identification via Adversarial Disentangling Learning

Despite the success of single-domain person re-identification (ReID), current supervised models degrade dramatically when deployed to unseen domains, mainly due to the discrepancy across cameras. To tackle this issue, we propose an Adversarial Disentangling Learning (ADL) framework to decouple camera-related and ID-related features, which can be readily used for camera-agnostic person ReID. ADL adopts a discriminative way instead of the mainstream generative styles in disentangling methods, eg., GAN or VAE based, because for person ReID task only the information to discriminate IDs is needed, and more information to generate images are redundant and may be noisy. Specifically, our model involves a feature separation module that encodes images into two separate feature spaces and a disentangled feature learning module that performs adversarial training to minimize mutual information. We design an effective solution to approximate and minimize mutual information by transforming it into a discrimination problem. The two modules are co-designed to obtain strong generalization ability by only using source dataset. Extensive experiments on three public benchmarks show that our method outperforms the state-of-the-art generalizable person ReID model by a large margin. Our code is publicly available at https://github.com/luckyaci/ADL_ReID.
Sporq: An Interactive Environment for Exploring Code using Query-by-Example

There has been widespread adoption of IDEs and powerful tools for program analysis. However, programmers still find it difficult to conveniently analyze their code for custom patterns. Such systems either provide inflexible interfaces or require knowledge of complex query languages and compiler internals. In this paper, we present Sporq, a tool that allows developers to mine their codebases for a range of patterns, including bugs, code smells, and violations of coding standards. Sporq offers an interactive environment in which the user highlights program elements, and the system responds by identifying other parts of the codebase with similar patterns. The programmer can then provide feedback which enables the system to rapidly infer the programmer’s intent. Internally, our system is driven by high-fidelity relational program representations and algorithms to synthesize database queries from examples. Our experiments and user studies with a VS Code extension indicate that Sporq reduces the effort needed by programmers to write custom analyses and discover bugs in large codebases.
Speculative Privacy Tracking (SPT): Leaking Information From Speculative Execution Without Compromising Privacy

Speculative execution attacks put a dangerous new twist on information leakage through microarchitectural side channels. Ordinarily, programmers can reason about leakage based on the program's semantics, and prevent said leakage by carefully writing the program to not pass secrets to covert channel-creating "transmitter" instructions, such as branches and loads. Speculative execution breaks this defense, because a transmitter might mis-speculatively execute with a secret operand even if it can never execute with said operand in valid executions.
SecureFS: A Secure File System for Intel SGX

A trusted execution environment or a TEE facilitates the secure execution of an application on a remote untrusted server. In a TEE, the confidentiality, integrity, and freshness properties for the code and data hold throughout the execution. In a TEE setting, specifically Intel SGX, even the operating system (OS) is not trusted. This results in certain limitations of a secure application's functionality, such as no access to the file system and network – as it requires OS support.
Deep learning-based detection of tax frauds: an application to property acquisition tax

Purpose Sampling taxpayers for audits has always been a major concern for policymakers of tax administration. The purpose of this study is to propose a systematic method to select a small number of taxpayers with a high probability of tax fraud. Design/methodology/approach An efficient sampling method for taxpayers for an audit is investigated in the context of a property acquisition tax. An autoencoder, a popular unsupervised learning algorithm, is applied to 2,228 tax returns, and reconstruction errors are calculated to determine the probability of tax deficiencies for each return. The reasonableness of the estimated reconstruction errors is verified using the Apriori algorithm, a well-known marketing tool for identifying patterns in purchased item sets. Findings The sorted reconstruction scores are reasonably consistent with actual fraudulent/non-fraudulent cases, indicating that the reconstruction errors can be utilized to select suspected taxpayers for an audit in a cost-effective manner. Originality/value The proposed deep learning-based approach is expected to be applied in a real-world tax administration, promoting voluntary compliance of taxpayers, and reinforcing the self-assessing acquisition tax system.
Analysis of Software Success Through Structural Equation Modeling

Determining factors influencing the success of software projects has been the emphasis of extensive research for more than 40 years. However, the majority of research in this domain has focused on developed countries, with little attention paid to underdeveloped and developing countries. The primary objective of this article was to assess the effect of critical elements on the success of software projects in underdeveloped countries (like Pakistan), because enterprise environmental factors and staff working habits, as well as their experience and expertise level, all have an effect on a project's success. For this purpose, data were collected from 339 senior developers and project managers working in Pakistan Software Export Board (PSEB) registered software companies. Structural Equation Modelling (SEM) was used to analyze the constructs and to assess the relationship between factors affecting software success. The empirical results showed that improper planning, inadequate human resources, wrong estimation of time and cost significantly negatively impacted the success of software projects. This research has opened new doors to extend our work in the software community to ultimately succeed in software projects.
Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data

The Natural Scene derived Spatial Frequency Response (NS-SFR) is a novel camera system performance measure that derives SFRs directly from images of natural scenes and processes them using ISO12233 edge-based SFR (e-SFR) algorithm. NS-SFR is a function of both camera system performance and scene content. It is measured directly from captured scenes, thus eliminating the use of test charts and strict laboratory conditions. The effective system e-SFR can be subsequently estimated from NS-SFRs using statistical analysis and a diverse dataset of scenes. This paper first presents the NS-SFR measuring framework, which locates, isolates, and verifies suitable step-edges from captures of natural scenes. It then details a process for identifying the most likely NS-SFRs for deriving the camera system e-SFR. The resulting estimates are comparable to standard e-SFRs derived from test chart inputs, making the proposed method a viable alternative to the ISO technique, with potential for real-time camera system performance measurements.
Temporal Graph Convolutional Network for Multimodal Sentiment Analysis

In this paper, we propose a temporal graph convolutional network (TGCN) to recognize the sentiments from language (textual), acoustic, and visual (facial expressions) modalities. TGCN constructs a modality-specific graph whose nodes are the aligned segments in the multimodal utterances and edges are weighted according to the distances between their features, in order to learn node embeddings with sequential semantics underlying the utterances. In particular, we use positional encoding by interleaving sine and cosine embedding to encode the positions of the segments in the utterances into their features. Given the modality-specific embeddings of the segments in utterances, we create an attention mechanism corresponding to the segments to capture the sentiment-related ones and obtain the unified embeddings of utterances. Furthermore, we fuse the attended embeddings of the multimodel utterances and conduct the attention to capture their interaction. Finally, the fused embeddings together with their raw features are concatenated together for sentiment predictions. Extensive experiments on three publicly available datasets show that TGCN outperforms the state-of-the-art methods.
Geochronological constraints on the geological history and gold mineralization in the Tick Hill region, Mt Isa Inlier

The Tick Hill Gold deposit in the southern Mary Kathleen Domain of the Mount Isa Inlier is hosted in a strongly deformed, Paleoproterozoic volcano-sedimentary sequence intruded by pre- and syn-tectonic granites. Igneous rocks and quartzite from the Tick Hill region were dated to constrain the age of the lithologies, deformation events, and gold mineralization. LA-ICP-MS, U-Pb zircon ages for these rocks, together with field relationships, confirm the presence of: (1) 1855–1850 granite belonging to the Kalkadoon Supersuite at ∼10 km and ∼4 km west of Tick Hill, respectively; (2) 1790–1770 Ma early syn-tectonic granite along the contact zone between mapped Argylla Formation and the Kalkadoon Supersuite west of Tick Hill, and later syn-tectonic leucogranite within the immediate vicinity of Tick Hill, including the host rocks to gold mineralization; and (3) late-tectonic, 1525–1520 Ma pegmatite and associated hydrothermal activity in the Tick Hill area that resulted in the mobilization of gold. Quartzite ridges in the hanging wall and footwall of the orebody provided contrasting results, with the youngest zircon population groupings at ca. 1781 Ma and 1841 Ma, respectively. Textural evidence suggests that much of the hanging wall quartzite is probably metasomatic in origin, and the ca. 1781 Ma age group, which was derived from mostly prismatic, euhedral zircon, reflects the age of a heavily silicified quartzofeldspathic gneiss. Thus, the age of the youngest detrital zircon group, ca. 1841 Ma, constrains the maximum age of the sequence at Tick Hill. Field evidence suggests that the 1790–1770 Ma granites intruded into the sedimentary sequence that hosts gold mineralization, indicating that the supracrustal rocks are at least ca. 1790 Ma in age and should not be grouped as Corella Formation. These sediments were affected by intense shearing and upright folding between 1790 Ma and 1770 Ma and younger normal faulting and metasomatism around 1525–1520 Ma. Early gold was introduced during D1 peak metamorphism at 1790–1770 Ma while the later mineralizing events involved the new introduction of gold or the remobilization of pre-existing older gold around 1525–1520 Ma. One of the major outcomes of this study is that the old schist zones in the south Mary Kathleen Domain are prospective for gold mineralization.
Design and Construction of a Low Cost of Solar Tracker Two Degree of Freedom (DOF) Based On Arduino

Recently, there has been an increase in the demand for solar panel installations. Unfortunately, it is mostly in a fixed surface or is in a stationary position. Hence, the power generated by the photo-voltaic (PV) cells decreases significantly according to different variations in solar radiation throughout the day. To increase the solar power generation, this paper proposes the design and construction of a two low-cost DOF solar tracker that follows the direction of the sun with a simple construction yet great precision. The system uses a closed- loop system where the light intensity is used as a reference for comparison by the Light Dependent Resistor (LDR) as an analog input to the Arduino and as a system controller. The tracking strategy uses the LDR orientation capability which can be divided into four directions, consisting of one direction, two directions, three directions and four directions of sunlight incident to the LDR sensor. The solar tracking system used is implemented in the Pseudo-Azimuths system which rotates from east to west and from north to south. In the experiment, the comparative evaluation between the 2 DOF solar tracking system and the static solar panel system is the main point of comparison in order to find out more reliable system. From the results of the tests, the results show that the proposed method increases the efficiency of the electrical energy of PV modules with an average value of 36.2% compared to a fixed model.
Machine Learning for Fraud Detection in E-Commerce: A Research Agenda

Fraud detection and prevention play an important part in ensuring the sustained operation of any e-commerce business. Machine learning (ML) often plays an important role in these anti-fraud operations, but the organizational context in which these ML models operate cannot be ignored. In this paper, we take an organization-centric view on the topic of fraud detection by formulating an operational model of the anti-fraud departments in e-commerce organizations. We derive 6 research topics and 12 practical challenges for fraud detection from this operational model. We summarize the state of the literature for each research topic, discuss potential solutions to the practical challenges, and identify 22 open research challenges.