Reviewerly/exHarmony huggingface dataset:
Outcomes of Deep Neural Network Hyperparameter Tuning on Bengali Speech Token Classification

Speech-based human-computer interaction is a vital research area where Bengali speech requires substantial research for being efficient like English. Nowadays, most speech-related research involves Deep Neural Networks (DNNs). We present comprehensive results on DNN hyperparameter tuning on isolated Bengali vowel and word classification. Two separate datasets of isolated Bengali vowels and words are utilized in a four-hidden-layered DNN configuration. Five formant frequencies have been extracted for all fourteen vowels and words, and after applying a necessary feature standardization technique, these acoustic features are utilized in the DNN input layer. Three hyperparameters–optimizer, batch size, and dropout–have been tuned to develop an optimum DNN classification model. We report the outcome achieved after applying different sets of values for these three hyperparameters. Finally, we recommend an Adadelta optimizer, a batch size of 128 or 256, and a dropout between 0.25 to 0.4 after the first hidden layer. The DNN formed by utilizing these recommended hyperparameters will be a potential pre-configured speech classifier for various humancomputer interaction-based devices and systems.
Effects of Provenance, Transport Processes and Chemical Weathering on Heavy Mineral Composition: A Case Study From the Songhua River Drainage, NE China

Understanding the heavy mineral composition of the Songhua River basin in NE China and the influencing factors (e.g., provenance, transport processes and chemical weathering) is crucial for the study of both the source-to-sink processes and the drainage evolution in the region. To this end, a total of 43 samples were collected from the river bars and terraces of the main and tributary streams of the Songhua River, and analyzed for heavy minerals in different grain-size fractions based on the novel automated TESCAN Integrated Mineral Analyzer (TIMA) combined with standard optical method. The results show that the tributaries originating from different mountains have significantly different heavy mineral composition. The locally occurring basic source signal in the tributaries of the Nenjiang River (the largest tributary of the Songhua River) are not well preserved in the Nenjiang River and the Songhua River trunk streams, indicating that the control of sources on heavy mineral composition is influenced by fluvial processes. Additionally, significant differences in the heavy mineral composition of different reaches of the same river also indicate that the heavy mineral composition is significantly influenced by fluvial processes. Influenced by hydraulic sorting during river processes, heavy minerals are enriched in different size fractions with the low-density minerals systematically overestimated in a wide window, suggesting an advantage of multi-window policy over wide window policy. In contrast to modern river sediments, the original heavy mineral composition of river terrace sediments has been severely damaged due to chemical weathering, so the degree of chemical weathering of terrace sediments needs to be evaluated first in provenance tracing and paleo-drainage evolution studies. TIMA has an irreplaceable role in identifying mineral species, additional images and elemental composition, and however, it performs poorly in identifying polycrystalline minerals, thus the combination with traditional methods can obtain more complete and accurate information.
User Preference Modeling on Heterogeneous Implicit Feedback with Transfer Learning<sup>1</sup>

User preference modeling from heterogeneous implicit feedback (i.e. various user behaviors) is a key issue in recommender systems, especially when homogeneous implicit feedback is sparse or even not available. But different types of implicit feedback have different confidence in user preference indication, which bring difficulties in building accurate user preference models in recommender system.In this article, we propose to model user preference profiles based on heterogeneous implicit feedback from a new perspective, in which preference confidence from different user implicit feedback should be measured in a unified way. Specifically, a preference confidence transfer learning algorithm based on multiple types of user behavior is designed. We assume that preference confidence can be transferred between target behavior and auxiliary behavior based on the features of user behavior. Then we integrated the assumption into a Latent Factor Model with confidence. Furthermore, we conduct extensive empirical studies with various baseline methods on two real public datasets and find that our algorithm can perform significantly more accurate than the other methods.
Byzantine-Robust Private Information Retrieval with Low Communication and Efficient Decoding

A b Byzantine-robust K-out-of-ℓ private information retrieval ((b, K,ℓ)-BRPIR) scheme allows a user to retrieve any item of a database from ℓ servers, even if only K out of the ℓ servers respond and at most b out of the K responding servers provide false answers. The existing BRPIR schemes require either an exponential time decoding algorithm or a communication complexity no better than O(n1/(2k-1) k ℓ log ℓ) with k=K-2b. In this paper, we show a new (b,K,ℓ)-BRPIR scheme that has both a polynomial time decoding algorithm and a communication cost of ℓ • exp(O((log n)1-over 1 r (log log n) 1 overr)) for r=log (K/(2b+1)), which is more efficient when n→ ∞.
A highly reliable cross-domain identity authentication protocol based on blockchain in edge computing environment

Aiming at the urgent need for a cross-domain authentication scheme with low computational cost, low latency, and high security in the edge computing environment, this paper proposes a blockchain-based cross-domain authentication protocol. In this protocol, the IoT devices in the edge computing environment themselves control the generation, encrypted storage, registration, use or cancellation of their identities, and the authenticity and credibility of these identities can be guaranteed through a trusted identity checking mechanism. The public information of these identities is shared among edge nodes of different domains using the consensus mechanism of blockchain. IoT devices can perform cross-domain authentication at the edge. The authentication process not only makes use of the advantages of low latency of edge computing, but also minimizes the disclosure of detailed identity information. Security analysis shows that the protocol can resist common attacks such as DDoS Attacks and Impersonation Attacks. Compared with several previous cross-domain authentication schemes, our protocol is more secure. Besides, this protocol has the advantage of low computational cost, suitable for low-performance IoT devices.
An Automatical And Efficient Image Classification Based On Improved Genetic Programming

Image classification is a basic task in machine intelligence, but challenging due to high variations across images. Traditional methods use hand-crafted features to solve it, which require much domain knowledge. Genetic Programming (GP) can automatically solve problems without much knowledge about the structure and form of the solution. And GP is interpretable and needs less time to adjust the parameters compared with deep image classification methods. However, the existing GP-based image classification methods have some disadvantages, such as poor classification performance and long training time. This paper proposed a new image classification algorithm based on multilayer genetic programming with cache (MCGP). MCGP designs a new hierarchical individual program structure with a classification layer and uses a subtree cache strategy to reduce training time. The experiments show that MCGP can get better or competitive results compared with traditional methods, other GP methods, and convolutional neural network methods. In addition, the training speed of MCGP is much faster than other GP methods.
Cervical Cancer Cell Image Detection Method Based on Improved YOLOv4

Aiming at the problem of frequent false and missed detection due to the dense and scattered distribution of cancer cells in the pathological images of cervical cancer cells, the complex image background, and the rich proportion of small objects, an improved image detection method of cervical cancer cells is proposed by combining the advantage of convolutional neural network and the transformer on the basis of the YOLOv4 network. Firstly, this method introduces the CoT module into the backbone feature extraction network of YOLOv4 to improve the global information acquisition ability of the model and the visual expression ability of the model. Secondly, according to the characteristics of the cervical cancer cell object detection dataset, data augmentation strategies such as Mosaic are used to enrich the dataset and improve the generalization ability of the model. Lastly, The improved method proposed is used to perform experiments on cervical cancer cell image dataset, and the corresponding results are obtained. The results show that the improved network model is significantly improved, which significantly improves the detection ability of cervical cancer cells and fully verifies the effectiveness of the algorithm proposed in this paper.
RecIPE: Revisiting the Evaluation of Memory Error Defenses

Many detection and defense mechanisms have been proposed to prevent and mitigate memory errors. A good understanding of the pros and cons of memory defense mechanisms is essential before practical deployment. However, the environment, compilers, and defenses change over time. Thus, a benchmark providing comprehensive evaluations of a range of compilation option choices and defenses is helpful to give developers guidance. The most well-known test suite for evaluating spatial memory errors and exploits is RIPE. However, we show that it is no longer applicable, and a new benchmark is needed. Furthermore, a benchmark should be extensible to evolve over time easily. We propose RecIPE, a new extensible and comprehensive benchmark for evaluating memory error defenses. For extensibility and customisability, RecIPE consists of two components. The TestGen component generates vulnerable code from configurable templates across various vulnerable attributes allowing easy changes through the templates. Measuring the attack and exploitation is with the DefEval component, serving as an attacker at runtime and can be updated with new exploitations. We present a comprehensive evaluation of compiler defenses and sanitizers on RecIPE with gcc and clang, showing the strengths and weaknesses of the various defenses. The results show pros and cons which may not be well known, including gaps between the in-principle guarantee and practical implementation. Our results also point out directions for further improvement in defenses.
CSMRS: An Efficient and Effective Semantic-aware Ranked Search Scheme over Encrypted Cloud Data

The document vectors constructed by the traditional searchable encryption scheme based on the term frequency-inverse document frequency model not only have high dimensionality and sparsity, but also ignore the semantic information of documents and keywords. In this paper, we introduce the sentence bidirectional encoder representations from transformers model (SBERT) to obtain semantic information-embedded vectors for documents and keywords. By adopting the SBERT model, we pro-pose a CBG-index based semantic-aware multi-keyword ranked search scheme (CSMRS). In the scheme, a topic-term frequency-inverse topic frequency (TTF-ITF) model and a clustering-based group index (CBG-index) are proposed. The TTF-ITF model is used to generate semantic vectors for keywords, and the CBG-index is used to improve the search efficiency. The experimental results demonstrate the better performance than the existing works in terms of search efficiency and search result semantic precision.
Online Feedback Systems and Debate on Scientific Issues during COVID-19: A Case Study on Sookmyung Women’s University

As the COVID-19 pandemic continues, university education and feedback guidance have inevitably moved to online platforms, becoming a global trend. This study focuses on a case of Sookmyung Women's University in South Korea, which has operated an online discussion clinic for university general education for more than a year as a case study. There are two main research methods. A frequency analysis was conducted to confirm what kind of counseling the students preferred at the discussion clinic based on the answers written in the students' applications. The students whose applications were used for the analysis were divided into 57 teams, and there were two to six members per team. The results were as follows: In the survey results, students wanted help with the preparation process necessary for the discussion and the practical strategies for facilitating discussions. They wanted personalized counseling, demonstrating that discussion education provided in the foundational curriculum is insufficient. Second, the educational model of the discussion clinic and educational examples were examined. The findings confirmed that online discussion education is effective if the system is technically supplemented. Instructors and researchers are prepared to meet students' demands for feedback and individual counseling, even if these are not provided through face-to-face discussions. Additionally, face-to-face guidance can be operated more effectively by taking advantage of online systems. The findings also demonstrate that further research on designing and operating online discussion centers is required. This study is a preceding study on developing online systems and educational guidelines for higher educational institutions to present new insights into smart learning. This paper also includes suggestions for educational and scientific discussions. The online discussion instructional model shown in this paper explores methods of scientific communication through a debate on scientific issues.
Wireless Manipulation of Serial Communication

Wired serial communication (e.g., UART, I2C) is widely used to exchange information between sensors, actuators, and controllers in automation, control, and cyber-physical systems. In this work, it is demonstrated that intentional electromagnetic interference (IEMI) can be utilized to not only induce spurious serial communications but to also alter legitimate communications, arbitrarily and at a distance, through attacks that cause controlled, bidirectional bit flips. To prove the efficacy of such attacks, two attack signal types, which require differing levels of attacker knowledge and resources to be effective, are proposed and evaluated against UART and I2C serial communication systems. The first attack waveform, which we call simple, is an inexpensive--to--produce narrowband waveform that has high power and tight timing constraints, but requires little attacker knowledge about the targeted system, while the second waveform, which we call complex, leverages a wideband signal that requires less power to achieve the same effect, is more tolerant of timing error in the signal processing phase, but requires a high amount of attacker knowledge of the targeted system. The simple waveform is shown to be over 98.3% effective at inducing a desired bit sequence into randomly transmitted UART frames, which indicates that an attacker could also choose to inject spurious UART frames, at will. On the I2C data streams, the complex waveform is demonstrated to be overall 75% effective in inducing random bits. Countermeasures are discussed and experimentally validated in high-IEMI scenarios.
Incomplete Multi-View Clustering Based on Weighted Adaptive Graph Learning

Multi-view clustering methods utilize complementary and consistent information among different views to classify samples into correct clusters. However, traditional multi-view clustering methods are proposed based on complete datasets. In practical applications, complete data samples rarely exist, and incomplete data are more common. This paper proposes an incomplete multi-view clustering algorithm based on weighted adaptive graph learning. Specifically, we first introduce a distance regularization term and integrate it into the framework of low-rank representations to learn graphs with both local and global structure of the data. Then, we use spectral clustering to achieve a low dimensional matrix for each graph. Finally, we use a weighted fusion mechanism to learn a consensus representation, and utilize the K-means algorithm to get the final clustering results. Experimental results on different incomplete datasets demonstrate the effectiveness of the algorithms.
Accounting Cyber Security Based on Blockchain

With the continuous development of digital economic life, cyber threats are increasing. The accounting system is the majority of enterprises in the economic process of the participants, and cyber security is the primary guarantee of accounting information security. Accounting processes are getting more complex so that computer communications technology is improving and cyber threats are rising. Accounting is required as the foundation of enterprise cyber security. The external cyber threat is to steal accounting data, especially commercial confidential data. The internal cyber threat is to whitewash accounting statements maliciously to obtain improper interests. This study focuses on accounting cyber security and links the use of blockchain to accounting cyber security issues. The research is based on the combined analysis of block chain and accounting cyber security. The main results are: proposed the advantages of blockchain to construct the accounting cyber security system, proposed a feasible solution to secure data of accounting. which contributes to the application of blockchain in the accounting field.
InfoCensor

Deep learning sits at the forefront of many on-going advances in a variety of learning tasks. Despite its supremacy in accuracy under benign environments, Deep learning suffers from adversarial vulnerability and privacy leakage (e.g., sensitive attribute inference) in adversarial environments. Also, many deep learning systems exhibit discriminatory behaviors against certain groups of subjects (e.g., demographic disparity). In this paper, we propose a unified information-theoretic framework to defend against sensitive attribute inference and mitigate demographic disparity in deep learning for the model partitioning scenario, by minimizing two mutual information terms. We prove that as one mutual information term decreases, an upper bound on the chance for any adversary to infer the sensitive attribute from model representations will decrease. Also, the extent of demographic disparity is bounded by the other mutual information term. Since direct optimization on the mutual information is intractable, we also propose a tractable Gaussian mixture based method and a gumbel-softmax trick based method for estimating the two mutual information terms. Extensive evaluations in a variety of application domains, including computer vision and natural language processing, demonstrate our framework's overall better performance than the existing baselines.
Deep Learning for Depression Symptomatic Activity Recognition

Discovering solutions to a handful of hazards associated with human health through analyzing physical activities have elevated the aspiration of prognosis of various diseases. This novel approach employs Human Activity Recognition (HAR) to devise depression symptomatic activities as a repercussion. The accumulation of data is conducted versatilely, including outdoor and indoor activities, maintaining all desirable postures while the smartphone is located in the slash pocket. Concerning the augmentation of the original dataset, synthetic sensors data are generated by the Generative Adversarial Network (GAN) model.Data preprocessing, including Butterworth low pass filter, enhanced the dataset. Considering the type of data being sequential, deep learning models like Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were employed. The evaluation process is held in three stages. Firstly, LSTM outperformed GRU on the amalgamation of actual and generated data with an accuracy of 96.48%. Secondly, the selected dataset was further experimented to recognize the significance of the Butterworth low pass filter, where it achieved greater accuracy of 2.01% with its presence. Finally, two publicly available datasets WISDM and MHEALTH were compared with the acquired dataset based on the two proposed models, where the combination of the depression dataset and LSTM model acquired higher accuracy of 1.80%.
Efficient Oblivious Permutation via the Waksman Network

Memory accesses to data stored on an untrusted server are known to leak information, even if the data is encrypted. The oblivious permutation (OP) is a key primitive for algorithms and protocols that are designed to hide these client accesses to the server. An OP algorithm permutes outsourced data blocks according to a given permutation without revealing the permutation to the server.
An Improved Reinforcement Learning Method for Underwater Image Enhancement

When light travels in water, it attenuates and causes phenomena such as absorption and scattering so that underwater images have problems with color distortion, low contrast, and fuzzy details. Since the attenuation of light varies with the wavelength and largely depends on the type of water body and structure of the underwater scene, it is difficult to remove various interference factors to achieve the best effect. We analyzed the current underwater enhancement and restoration algorithms, finding that the effectiveness of most methods requires further improvement and some methods have limited application in terms of scope. Therefore, we innovatively adopted a reinforcement learning method combined with underwater presentation characteristics to adaptively learn the most reasonable enhancement strategy and used evaluation criteria for underwater images as the feedback function of the reinforcement learning. This method further modifies the color, contrast, and detail of the image. It is also well-adapted to images with different turbidity conditions, water areas, and underwater environments, demonstrating robustness.
Anti-vertex for neighborhood constraints in subgraph queries

This paper focuses on subgraph queries where constraints are present in the neighborhood of the explored subgraphs. We describe anti-vertex, a declarative construct that indicates absence of a vertex, i.e., the resulting subgraph should not have a vertex in its specified neighborhood that matches the anti-vertex. We formalize the semantics of anti-vertex to benefit from automatic reasoning and optimization, and to enable standardized implementation across query languages and runtimes. The semantics are defined for various matching semantics that are commonly employed in subgraph querying (isomorphism, homomorphism, and no-repeated-edge matching) and for the widely adopted property graph model. We illustrate several examples where anti-vertices can be employed to help familiarize with the anti-vertex concept. We further showcase how anti-vertex support can be added in existing graph query languages by developing prototype extensions of Cypher language. Finally, we study how anti-vertices interact with the symmetry breaking technique in subgraph matching frameworks so that their meaning remains consistent with the expected outcome of constrained neighborhoods to connected vertices.