Reviewerly/exHarmony huggingface dataset:
Recognition of Rock Micro-Fracture Signal Based on Deep Convolution Neural Network Inception Algorithm

Rockburst is a common geological disaster in mines, tunnels, deep underground engineering, and during excavation, mining, and construction.Rockburst frequently occurs as the depth of burial increases, and its early warning technology is in urgent need of further development.At present, the most effective monitoring and analysis method of rockburst is microseismic technology, which detects a large number of rock micro-fracture signals through geophones.The identification of microseismic monitoring data is an essential part of microseismic data processing.It is necessary to identify effective microseismic signals from considerable monitoring data for subsequent early warning.Aiming at the identification of rock micro-fracture signals, this thesis proposes a microseismic data identification method based on the Deep Convolution Neural Network Inception (DCNN-Inception) algorithm.The algorithm uses an existing Convolutional Neural Network (CNN) model, adding Inception structure in the middle of the model to form a DCNN-Inception model.A data set was established depending on the actual measured data of Baihetan Hydropower Station, and CNN and DCNN-Inception were employed to identify effective microseismic signals.The results demonstrate that the DCNN-Inception algorithm is better than CNN in recognition accuracy and can effectively identify effective microseismic signals.It provides an essential foundation for the identification of microseismic abnormal signals of rock microfracture and the early warning of rock rupture precursors and is of practical significance for the study of rockburst warning technology.
Multilinear cryptography using nilpotent groups

In this paper, we develop a novel idea of multilinear cryptosystem using nilpotent group identities.
Open Learner Models for Multi-activity Educational Systems

In recent years, there has been an increasing trend in the use of student-centred approaches within educational systems that engage students in various higher-order learning activities such as creating resources, creating solutions, rating the quality of resources, and giving feedback. In response to this trend, this paper proposes an interpretable and open learner model called MA-Elo that capture an abstract representation of a student’s knowledge state based on their engagement with multiple types of learning activities. We apply MA-Elo to three data sets obtained from an educational system supporting multiple student activities. Results indicate that the proposed approach can provide a higher predictive performance compared with baseline and some state-of-the-art learner models.
How Robust are Model Rankings : A Leaderboard Customization Approach for Equitable Evaluation

Models that top leaderboards often perform unsatisfactorily when deployed in real world applications; this has necessitated rigorous and expensive pre-deployment model testing. A hitherto unexplored facet of model performance is: Are our leaderboards doing equitable evaluation? In this paper, we introduce a task-agnostic method to probe leaderboards by weighting samples based on their 'difficulty' level. We find that leaderboards can be adversarially attacked and top performing models may not always be the best models. We subsequently propose alternate evaluation metrics. Our experiments on 10 models show changes in model ranking and an overall reduction in previously reported performance- thus rectifying the overestimation of AI systems' capabilities. Inspired by behavioral testing principles, we further develop a prototype of a visual analytics tool that enables leaderboard revamping through customization, based on an end user's focus area. This helps users analyze models' strengths and weaknesses, and guides them in the selection of a model best suited for their application scenario. In a user study, members of various commercial product development teams, covering 5 focus areas, find that our prototype reduces pre-deployment development and testing effort by 41% on average.
Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction

This paper asks whether a distinction between production-based and perception-based grammar induction influences either (i) the growth curve of grammars and lexicons or (ii) the similarity between representations learned from independent sub-sets of a corpus.A productionbased model is trained on the usage of a single individual, thus simulating the grammatical knowledge of a single speaker.A perception-based model is trained on an aggregation of many individuals, thus simulating grammatical generalizations learned from exposure to many different speakers.To ensure robustness, the experiments are replicated across two registers of written English, with four additional registers reserved as a control.A set of three computational experiments shows that production-based grammars are significantly different from perception-based grammars across all conditions, with a steeper growth curve that can be explained by substantial inter-individual grammatical differences.
Stochastic Sign Descent Methods: New Algorithms and Better Theory

Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such as signSGD, have recently been gaining popularity because of their simple compression rule and connection to adaptive gradient methods, like ADAM. In this paper, we analyze sign-based methods for non-convex optimization in three key settings: (i) standard single node, (ii) parallel with shared data and (iii) distributed with partitioned data. For single machine case, we generalize the previous analysis of signSGD relying on intuitive bounds on success probabilities and allowing even biased estimators. Furthermore, we extend the analysis to parallel setting within a parameter server framework, where exponentially fast noise reduction is guaranteed with respect to number of nodes, maintaining $1$-bit compression in both directions and using small mini-batch sizes. Next, we identify a fundamental issue with signSGD to converge in distributed environment. To resolve this issue, we propose a new sign-based method, {\em Stochastic Sign Descent with Momentum (SSDM)}, which converges under standard bounded variance assumption with the optimal asymptotic rate. We validate several aspects of our theoretical findings with numerical experiments.
Reviewing Influence of UTAUT2 Factors on Cyber Security Compliance: A Literature Review

Evidence suggests that, regardless of the number of technical controls in place, organizations will still experience security breaches. Organizations spend millions of dollars on their cyber security infrastructure that includes technical and non-technical measures but mostly disregarded the most important asset and vulnerability the human.
Modeling Event Plausibility with Consistent Conceptual Abstraction

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events.While distributional models-most recently pre-trained, Transformer language modelshave demonstrated improvements in modeling event plausibility, their performance still falls short of humans'.In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that "a person breathing" is plausible while "a dentist breathing" is not, for example.We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization

Abstractive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of information scattered across the long document, and (2) composing a cohesive text by reconstructing these salient facts into a shorter summary that faithfully reflects the complex relations connecting these facts.In this paper, we adapt TP-TRANSFORMER (Schlag et al., 2019), an architecture that enriches the original Transformer (Vaswani et al., 2017) with the explicitly compositional Tensor Product Representation (TPR), for the task of abstractive summarization.The key feature of our model is a structural bias that we introduce by encoding two separate representations for each token to represent the syntactic structure (with role vectors) and semantic content (with filler vectors) separately.The model then binds the role and filler vectors into the TPR as the layer output.We argue that the structured intermediate representations enable the model to take better control of the contents (salient facts) and structures (the syntax that connects the facts) when generating the summary.Empirically, we show that our TP-TRANSFORMER outperforms the Transformer and the original TP-TRANSFORMER significantly on several abstractive summarization datasets based on both automatic and human evaluations.On several syntactic and semantic probing tasks, we demonstrate the emergent structural information in the role vectors and improved syntactic interpretability in the TPR layer outputs.
Multi-Layer Fusion Neural Network for Deepfake Detection

Recently, the spread of videos forged by deepfake tools has been widely concerning, and effective ways for detecting them are urgently needed. It is known that such artificial intelligence-aided forgery makes at least three levels of artifacts, which can be named as microcosmic or statistical features, mesoscopic features, and macroscopic or semantic features. However, existing detection methods have not been designed to exploited them all. This work proposes a new approach to more effective detection of deepfake videos. A multi-layer fusion neural network (MFNN) has been designed to capture the artifacts in different levels. Features maps output from specially designed shallow, middle, and deep layers, which are used as statistical, mesoscopic, and semantic features, respectively, are fused together before classification. FaceForensic++ dataset was used to train and test the method. The experimental results show that MFNN outperforms other relevant methods. Particularly, it demonstrates more advantage in detecting low-quality deepfake videos.
Heavy metals pollution indexing, geospatial and statistical approaches of groundwater within Challawa and Sharada industrial areas, Kano City, North-Western Nigeria

Abstract The present study focused on pollution status of groundwater in the industrial areas of Challawa and Sharada in Kano city based on pollution indices, statistical and spatial analyses. Twenty groundwater samples representing groundwater of the studied areas (Ten from each area) were analyzed for the presence of Cd, Cr, Ni, Fe, Mn and Zn using atomic absorption spectrophotometer. The result showed 95%, 5%, 60%, 15% and 25% of the analyzed water samples had detectable Cd, Cr, Ni, Fe, and Mn above the drinking water limits of both Nigerian standards for drinking water quality NSDWQ and World Health Organization (WHO) with Cd dominating other analyzed heavy metals in the groundwater. Evaluation of heavy metal pollution revealed a low polluted status based on the contaminant index (C d ), synthetic pollution index, heavy metals evaluation index, and heavy metal pollution index. Metal index categorized the groundwater as seriously polluted. The statistical evaluation gave strong and positive correlations between indices and a moderate one between the metallic ions. Component analysis revealed a strongly positive loading of Fe, Ni and Zn while Cd had a strong negative loading. Cr and Mn were positive and moderately loaded. Statistical analyses suggested both anthropogenic and geogenic sources for the heavy metals mainly from the industrial and agricultural practices and rock weathering processes, respectively. This study is expected to be a useful tool in the planning, monitoring and mitigation of pollution activities in the area. Article Highlights The pollution status of groundwater with respect to heavy metals was investigated in the Challawa and Sharada industrials zones in Kano city Nigeria The concentration of Cd, Fe, Ni, Cr Mn and Zn was determined using the AAS Different Pollution indices of HPI, HEI, SPI, C d and MI were utilized to categorized the area as low, medium and highly polluted. Spatial and temporal distribution maps demarcated based on the metal concentrations and computed indices in the area. CA, PCA, and HCA were used to identified the geochemistry, relationship, sources and origin of heavy metals in groundwater. The study revealed zones with low to high-risk groundwater in terms of toxic heavy metals and pollution status.
Compact quantum random number generation using a linear optocoupler

To date, various quantum random number schemes have been demonstrated. However, the cost, size, and final random bit generation rate usually limits their wide application on-shelf. To overcome these limitations, we propose and demonstrate a compact, simple, and low-cost quantum random number generation based on a linear optocoupler. Its integrated structure consists mainly of a light emitting diode and a photodetector. Random bits are generated by directly measuring the intensity noise of the output light, which originates from the random recombination between holes of the p region and electrons of the n region in a light emitting diode. Moreover, our system is robust against fluctuation of the operating environment, and can be extended to a parallel structure, which will be of great significance for the practical and commercial application of quantum random number generation. After post-processing by the SHA-256 algorithm, a random number generation rate of 43 Mbps is obtained. Finally, the final random bit sequences have low autocorrelation coefficients with a standard deviation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>3.16</mml:mn> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> and pass the NIST-Statistical Test Suite test.
Adaptive multi-scale feature fusion based U-net for fracture segmentation in coal rock images

Accurate segmentation of fractures in coal rock CT images is important for the development of coalbed methane. However, due to the large variation of fracture scale and the similarity of gray values between weak fractures and the surrounding matrix, it remains a challenging task. And there is no published dataset of coal rock, which make the task even harder. In this paper, a novel adaptive multi-scale feature fusion method based on U-net (AMSFF-U-net) is proposed for fracture segmentation in coal rock CT images. Specifically, encoder and decoder path consist of residual blocks (ReBlock), respectively. The attention skip concatenation (ASC) module is proposed to capture more representative and distinguishing features by combining the high-level and low-level features of adjacent layers. The adaptive multi-scale feature fusion (AMSFF) module is presented to adaptively fuse different scale feature maps of encoder path; it can effectively capture rich multi-scale features. In response to the lack of coal rock fractures training data, we applied a set of comprehensive data augmentation operations to increase the diversity of training samples. These extensive experiments are conducted via seven state-of-the-art methods (i.e., FCEM, U-net, Res-Unet, Unet++, MSN-Net, WRAU-Net and ours). The experiment results demonstrate that the proposed AMSFF-U-net can achieve better segmentation performance in our works, particularly for weak fractures and tiny scale fractures.
Provable Security of SP Networks with Partial Non-Linear Layers

Motivated by the recent trend towards low multiplicative complexity blockciphers (e.g., Zorro, CHES 2013; LowMC, EUROCRYPT 2015; HADES, EUROCRYPT 2020; MALICIOUS, CRYPTO 2020), we study their underlying structure partial SPNs, i.e., Substitution-Permutation Networks (SPNs) with parts of the substitution layer replaced by an identity mapping, and put forward the first provable security analysis for such partial SPNs built upon dedicated linear layers. For different instances of partial SPNs using MDS linear layers, we establish strong pseudorandom security as well as practical provable security against impossible differential attacks. By extending the well-established MDS code-based idea, we also propose the first principled design of linear layers that ensures optimal differential propagation. Our results formally confirm the conjecture that partial SPNs achieve the same security as normal SPNs while consuming less non-linearity, in a well-established framework.
WEB BASED DATABASE SECURITY IN INTERNET OF THINGS USING FULLY HOMOMORPHIC ENCRYPTION AND DISCRETE BEE COLONY OPTIMIZATION

Web applications are utilized on an extensive scale across the globe and it handles sensitive individual information of users. Structured Query Language (SQL) Data Inference (DI) and injection are procedures that abuse a security defenselessness occurring in the database layer of an application. This research article focuses on website page database security with the help of optimization and encryption methods for Web of Things Environments. Initially, the selected queries in webpage application are injected as per Discrete Bee Colony Optimization (DBCO) procedure. After the Proxy filtering, the injection prevention model is utilized, the injected data with various queries of different special characters are utilized. At long last, the attack gets detected depending on the user query with the assistance of query tree mechanism. Besides, an effective Fully Homomorphic Encryption (FHE) encryption is proposed in the study. From the implementation results, it is to be noted that the proposed method achieved 93.56% security level for the prevented webpage implication-based databases. The effect on the businesses must be comprehended to decrease the risk involved in SQL and DI injection assaults.
Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality

We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context.For writing, lexical substitution systems can assist humans by suggesting words that humans cannot easily think of.However, existing benchmarks depend on human recall as the only source of data, and therefore lack coverage of the substitutes that would be most helpful to humans.Furthermore, annotators often provide substitutes of low quality, which are not actually appropriate in the given context.We collect higher-coverage and higher-quality data by framing lexical substitution as a classification problem, guided by the intuition that it is easier for humans to judge the appropriateness of candidate substitutes than conjure them from memory.To this end, we use a contextfree thesaurus to produce candidates and rely on human judgement to determine contextual appropriateness.Compared to the previous largest benchmark, our SWORDS benchmark has 3x as many substitutes per target word for the same level of quality, and its substitutes are 1.4x more appropriate (based on human judgement) for the same number of substitutes.
A privacy-preserving design for sharing demand-driven patient datasets over permissioned blockchains and P2P secure transfer

Sharing patient datasets curated by health institutions is critical for the advance of monitoring, surveillance and research. However, patient data is sensitive data and it can only be released under certain conditions and with previous explicit consent. Privacy preserving data sharing provides techniques to distribute datasets minimizing the risk of identification of patients. However, the sharing of datasets is typically done without considering the needs or requests of data consumers. Blockchain technologies provide an opportunity to gather those requests and share and assemble datasets using privacy-preserving methods as data and requirements on anonymity match. The architecture and design of such a solution is described, assuming an underlying permissioned blockchain network where providers such as healthcare institutions deal with consent, patient preferences and anonymity guarantees, playing a mediator role to a network of organizations.
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks

Spoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system.Recent research shows promising results by jointly learning of those two tasks based on the fact that slot filling and intent detection are sharing semantic knowledge.Furthermore, attention mechanism boosts joint learning to achieve state-of-the-art results.However, current joint learning models ignore the following important facts: 1.Long-term slot context is not traced effectively, which is crucial for future slot filling.2. Slot tagging and intent detection could be mutually rewarding, but bidirectional interaction between slot filling and intent detection remains seldom explored.In this paper, we propose a novel approach to model long-term slot context and to fully utilize the semantic correlation between slots and intents.We adopt a key-value memory network to model slot context dynamically and to track more important slot tags decoded before, which are then fed into our decoder for slot tagging.Furthermore, gated memory information is utilized to perform intent detection, mutually improving both tasks through global optimization.Experiments on benchmark ATIS and Snips datasets show that our model achieves state-of-the-art performance and outperforms other methods, especially for the slot filling task.
Survey of CPU Cache-Based Side-Channel Attacks: Systematic Analysis, Security Models, and Countermeasures

Privacy protection is an essential part of information security. The use of shared resources demands more privacy and security protection, especially in cloud computing environments. Side-channel attacks based on CPU cache utilize shared CPU caches within the same physical device to compromise the system’s privacy (encryption keys, program status, etc.). Information is leaked through channels that are not intended to transmit information, jeopardizing system security. These attacks have the characteristics of both high concealment and high risk. Despite the improvement in architecture, which makes it more difficult to launch system intrusion and privacy leakage through traditional methods, side-channel attacks ignore those defenses because of the shared hardware. Difficult to be detected, they are much more dangerous in modern computer systems. Although some researchers focus on the survey of side-channel attacks, their study is limited to cryptographic modules such as Elliptic Curve Cryptosystems. All the discussions are based on real-world applications (e.g., Curve25519), and there is no systematic analysis for the related attack and security model. Firstly, this paper compares different types of cache-based side-channel attacks. Based on the comparison, a security model is proposed. The model describes the attacks from four key aspects, namely, vulnerability, cache type, pattern, and range. Through reviewing the corresponding defense methods, it reveals from which perspective defense strategies are effective for side-channel attacks. Finally, the challenges and research trends of CPU cache-based side-channel attacks in both attacking and defending are explored. The systematic analysis of CPU cache-based side-channel attacks highlights the fact that these attacks are more dangerous than expected. We believe our survey would draw developers’ attention to side-channel attacks and help to reduce the attack surface in the future.