Reviewerly/exHarmony huggingface dataset:
Optimal construction of a functional interaction network from pooled library CRISPR fitness screens

Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these "coessentiality" networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process.In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step-essentiality scoring, sample variance and covariance normalization, and similarity measurement-to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from "moonlighting" proteins which show context-dependent interaction with different partners.We describe a systematic survey of methods for generating coessentiality networks from the Cancer Dependency Map data and provide a partial correlation-based approach for exploring context-dependent interactions.
Packet loss concealment method based on interpolation in packet voice coding

This paper proposes a packet loss concealment (PLC) method based on redundant information to enhance speech quality under severe network conditions as well as a new model to simulate packet loss effect, end-to-end transmission delay, and jitter on the concealment process. Our proposed method increases the efficiency of the standard ITU-T G.722.2 by improving the quality of the decoded speech under random frame loss conditions over packet-switched networks. The new method achieves a MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) value exceeding 48.7 at a 20% packet loss ratio (PLR). Numerical analysis indicates that our method outperforms existing alternatives and can be successfully used, even in severe propagation scenarios at the expense of an increase in processing requirements.
Ontology Concept Extraction Algorithm for Deep Neural Networks

An important drawback of deep neural networks limiting their application in critical tasks is the lack of explainability. Recently, several methods have been proposed to explain and interpret the results obtained by deep neural networks, however, the majority of these methods are targeted mostly at AI experts. Ontology-based explanation techniques seem promising, as they can be used to form explanations using domain terms (corresponding to ontology concepts) and logical statements, which is more understandable by domain experts. Recently, it has been shown, that inner representations (layer activations) of deep neural network can often be aligned with ontology concepts. However, not every concept can be matched with the output of every layer, and it can be computationally hard to identify the particular layer that can be easily aligned with the given concept, which is aggravated by the number of concepts in a typical ontology. The paper proposes an algorithm to address this problem. For each ontology concept it helps to identify neural network layer, which produces output that can be best aligned with the given concept. These connections can then be used to identify all the ontology concepts relevant to the sample and explain the network output in a user-friendly way.
Impact of Optimal Feature Selection Using Hybrid Method for a Multiclass Problem in Cross Project Defect Prediction

The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always trustworthy. Our goal is to predict the bugs in the new projects by training our model on the current projects for cross-projects to save time, cost, and effort. We used experimental research and the type of research is explanatory. Our research method is controlled experimentation, for which our independent variable is prediction accuracy and dependent variables are hyper-parameters which include learning rate, epochs, and dense layers of neural networks. Our research approach is quantitative as the dataset is quantitative. The design of our research is 1F1T (1 factor and 1 treatment). To obtain the results, we first carried out exploratory data analysis (EDA). Using EDA, we found that the dataset is multi-class. The dataset contains 11 different projects consisting of 28 different versions of all the projects in total. We also found that the dataset has significant issues of noise, class imbalance, and distribution gaps between different projects. We pre-processed the dataset for experimentation by resolving all these issues. To resolve the issue of noise, we removed duplication from the dataset by removing redundant rows. We then covered the data distribution gap between different sources and target projects using the min-max normalization technique. After covering the data distribution gap, we generated synthetic data using a CTGANsynthesizer to solve class imbalance issues. We solved the class imbalance issue by generating an equal number of instances, as well as an equal number of output classes. After carrying out all of these steps, we obtained normalized data. We applied the hybrid feature selection technique on the pre-processed data to optimize the feature set. We obtained significant results of an average AUC of 75.98%. From the empirical study, it was demonstrated that feature selection and hyper-parameter tuning have a significant impact on defect prediction accuracy in cross-projects.
A qualitative analysis of publicly available Standards and Guidance about Digital Literacies in U.S. States

Before the COVID-19 pandemic, schools had been adopting digital instruction in many parts of the world. The concept of digital literacies has also been evolving in complexity alongside the digital technologies that support it. However, little is known about what guidance available to support various levels of government in supporting digital literacies alongside digital instruction in local schools. The purpose of this study was to determine what guidance for digital literacies U.S. state departments of education had made available through their websites to local schools just prior to the onset of the pandemic. Using qualitative content analysis techniques, digital literacies guidance information was located on U.S. state departments of education websites and analyzed. Most states did not indicate that they used guidance from professional organizations about digital literacies. The 16 states that did have guidance used standards from the International Society for Technology in Education (ISTE), which have not been positioned by the organization as digital literacies standards, but instead reflect traditional understandings of Information Computing Technology (ICT). Implications of this study highlight potential strategies educational ministries might use to acknowledge and support digital literacies.
Wdt-SCAN: Clustering decentralized social graphs with local differential privacy

Social network users scatter geographically, where each maintains a self-centered star graph (i.e., relationships directed related to them). Clustering on such subgraphs can provide valuable insights into the graph structure. Despite its significance, one could not simply collect the unfettered star graph information from users due to privacy concerns. Local differential privacy (LDP), as an emerging privacy model, has been widely adopted in privacy-preserving data collection and analysis. Most existing LDP-based graph analysis methods encode star graphs via adjacency bit vector and suffer from heavy noise due to the sparsity of social networks. Besides, they cluster all nodes indifferently yet ignore that nodes with different degrees are affected differently by noise injection, making the clustering results unsatisfactory. To tackle these issues, we propose Wdt-SCAN, a degree vector based private graph clustering scheme to achieve high quality clustering without compromising individual privacy. Concretely, we design a degree vector encoding model with optimal length to represent star graphs, which reduces the massive noise caused by sparsity. To improve the clustering accuracy, a two-round agglomeration-based decentralized graph clustering method is proposed, which partitions nodes into core nodes and ordinary nodes, and then clusters core nodes to form a graph skeleton as prior knowledge to alleviate the impact of noise injection on ordinary nodes. Theoretical analysis and experiments on real-world datasets show that our proposed method can obtain desirable clustering result while satisfying edge LDP.
Protecting nonlocal quantum correlations in correlated squeezed generalized amplitude damping channel

Nonlocal quantum correlations, such as quantum entanglement, quantum steering, and Bell nonlocality, are crucial resources for quantum information tasks. How to protect these quantum resources from decoherence is one of the most urgent problems to be solved. Here, we investigate the evolution of these correlations in the correlated squeezed generalized amplitude damping (SGAD) channel and propose a scheme to protect them with weak measurement (WM) and quantum measurement reversal (QMR). Compared with the results of the uncorrelated SGAD channel, we find that when [Formula: see text], correlation and squeezing effects can prolong the survival time of quantum entanglement, Bell nonlocality, and quantum steering by about 152 times, 207 times, and 10 times, respectively. In addition, local WM and QMR can effectively recover the disappeared nonlocal quantum correlations either in uncorrelated or completely correlated SGAD channels. Moreover, we find that these initial nonlocal quantum correlations could be drastically amplified under the correlated channel. And the steering direction can be flexibly manipulated either by changing the channel parameters or the strength of WM and QMR. These results not only make a step forward in suppressing decoherence and enhancing quantum correlation in noise channels, but also help to develop relevant practical applications.
A Survey on the Security Issues of QUIC

A newly established multiplexed network protocol - QUIC, which is based on User Datagram Protocol (UDP), has emerged in recent years and gained a large share of Internet traffic quickly. Initially proposed by Google, the goal of QUIC is to achieve a higher Internet communication performance and eventually replace the Transmission Control Protocol (TCP) + Transport Layer Security (TLS) + HTTP/2 architecture. In particular, the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> version of the Hypertext Transfer Protocol - HTTP/3.0 is built on top of QUIC. A good number of research papers have been published recently to evaluate the performance and security of the QUIC protocol. In this paper, we conduct a comprehensive survey on the QUIC security issues and analyze its future research directions regarding security prospective. We investigate several topics including the QUIC protocol structure, QUIC security model, security issues related to QUIC protocol, and future research directions on QUIC Security. To the best of our knowledge, it is the one of first surveys that focus on the security of the QUIC protocol.
Speaker Identification Using Small Artificial Neural Network on Small Dataset

Speaker recognition provides an answer to the question “Who is speaking?”. Most of the research in the field of speaker recognition focuses on models for recognizing thousands of speakers and uses large datasets for doing so. In this paper, we explore the possibility of using small neural networks that could quickly be trained on small datasets for doing speaker recognition for a small number of speakers with high accuracy. To investigate this matter experimental analysis was conducted by using hyperparameter optimization to find the optimal combination of the structure and parameters for multiple configurations of neural networks. Furthermore, the investigation was performed with multiple preprocessing methods to find the effect of preprocessing on the size and accuracy of the resulting models. As the result of the experiment, we found a residual neural network with 387,461 parameters that was able to classify speakers with high accuracy. In comparison state of the art speaker recognition uses 4.2 million parameters.
Auditing blockchain smart contracts

The purpose of this paper is to present the main focus areas in smart contracts securing and to propose a procedure and a sample audit plan of an upload/download files smart contract on a blockchain-based platform designed for data and information sharing. Smart contracts and blockchain oracles definition and types are discussed and summarized. The smart contracts and blockchains securing issues and activities are systemized in four directions: code writing, security and penetration testing, blockchain security testing and vulnerability scanning tools. A five-step smart contract audit plan is suggested, adopted to the smart contract’s current functional specifications and program code complexity. The audit plan is illustrated by example.
Aerial Image Dehazing Using Reinforcement Learning

Aerial observation is usually affected by the Earth’s atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing. We first developed a clear–hazy aerial image dataset addressing various types of ground; we then compared the dehazing results of some state-of-the-art methods, including the classic dark channel prior, color attenuation prior, non-local image dehazing, multi-scale convolutional neural networks, DehazeNet, and all-in-one dehazing network. We extended the most suitable method, DehazeNet, to a multi-scale form and added it into a multi-agent deep reinforcement learning network called DRL_Dehaze. DRL_Dehaze was tested on several ground types and in situations with multiple haze scales. The results show that each pixel agent can automatically select the most suitable method in multi-scale haze situations and can produce a good dehazing result. Different ground scenes may best be processed using different steps.
Forecasting medical state transition using machine learning methods

Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original state, a transformed state is proposed in this research, which reflects how the state is transformed. The performance of the proposed method is compared with the original method under three models, including logistic regression, AdaBoost and XGBoost. The results show that the model XGBoost generally has the best performance measured by AUC, F1 and Sensitivity with values around 0.93, 0.91 and 0.90, at the prediction gaps 5, 10 and 20 separately. Under the model XGBoost, the method with transformed response variable has significantly better performance than that with the original response variable, with the performance metrics being around 1% to 4% higher, and the t values are all significant under the level 0.01. In order to explore the model performance under different baseline information, a subgroup analysis is conducted under sex, age, weight and height. The results demonstrate that sex and age have more significant influence on the model performance especially at the higher gaps than weight and height.
Mortality prediction in ICU Using a Stacked Ensemble Model

Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of various machine learning approaches in this field. Innovative algorithms play a crucial role in boosting the performance of models. This study uses a stacked ensemble model to predict mortality in ICU by incorporating the clinical severity scoring results, in which several machine learning algorithms are employed to compare the performance. The experimental results show that the stacked ensemble model achieves good performance compared with the model without integrating the severity scoring results, which has the area under curve (AUC) of 0.879 and 0.862, respectively. To improve the performance of prediction, two feature subsets are obtained based on different feature selection techniques, labeled as SetS and SetT. Evaluation performances show that the SEM based on the SetS achieves a higher AUC value (0.879 and 0.860). Finally, the SHapley Additive exPlanations (SHAP) analysis is employed to interpret the correlation between the risk features and the outcome.
In-Memory Distributed Mosaicking for Large-Scale Remote Sensing Applications with Geo-Gridded Data Staging on Alluxio

The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing.
The Effectiveness of Online Learning Using Zoom Meetings at Elementary Schools

This study aimed to describe the effectiveness of using Zoom Meetings, as one of the online learning platforms, in the learning process at the select school of elementary school. The purposive survey method was used in this study involving the process of data triangulation from tests, questionnaires, and unstructured interviews. Sampling in this study is random sample or probability sampling is a sampling technique that uses technical opportunities in the sampling process. A sample of 160 students select school participated in this study by filling out questionnaires distributed online. Student responses were analyzed descriptively quantitatively to determine the using of Zoom Meeting the implementation of learning. Data was collected using a five-item Likert scale survey. Based on the results of data analysis from filling in student questionnaires, it can be concluded that the online learning system using Zoom in elementary schools is effectively used, with the percentage of students understanding learning is 72.50%.
ECViST: Mine Intelligent Monitoring Based on Edge Computing and Vision Swin Transformer-YOLOv5

Mine video surveillance has a key role in ensuring the production safety of intelligent mining. However, existing mine intelligent monitoring technology mainly processes the video data in the cloud, which has problems, such as network congestion, large memory consumption, and untimely response to regional emergencies. In this paper, we address these limitations by utilizing the edge-cloud collaborative optimization framework. First, we obtained a coarse model using the edge-cloud collaborative architecture and updated this to realize the continuous improvement of the detection model. Second, we further proposed a target detection model based on the Vision Swin Transformer-YOLOv5(ViST-YOLOv5) algorithm and improved the model for edge device deployment. The experimental results showed that the object detection model based on ViST-YOLOv5, with a model size of only 27.057 MB, improved the average detection accuracy is by 25% compared to the state-of-the-art model, which makes it suitable for edge-end deployment in mining workface. For the actual mine surveillance video, the edge-cloud collaborative architecture can achieve better performance and robustness in typical application scenarios, such as weak lighting and occlusion, which verifies the feasibility of the designed architecture.
Trust your guts: fostering embodied knowledge and sustainable practices through voice interaction

Abstract Despite various attempts to prevent food waste and motivate conscious food handling, household members find it difficult to correctly assess the edibility of food. With the rise of ambient voice assistants, we did a design case study to support households’ in situ decision-making process in collaboration with our voice agent prototype, Fischer Fritz. Therefore, we conducted 15 contextual inquiries to understand food practices at home. Furthermore, we interviewed six fish experts to inform the design of our voice agent on how to guide consumers and teach food literacy. Finally, we created a prototype and discussed with 15 consumers its impact and capability to convey embodied knowledge to the human that is engaged as sensor. Our design research goes beyond current Human-Food Interaction automation approaches by emphasizing the human-food relationship in technology design and demonstrating future complementary human-agent collaboration with the aim to increase humans’ competence to sense, think, and act.