Reviewerly/exHarmony huggingface dataset:
A Collaborative Filtering Approach for Drug Repurposing

A recommendation system is proposed based on the construction of Knowledge Graphs, where physical interaction between proteins and associations between drugs and targets are taken into account. The system suggests new targets for a given drug depending on how proteins are linked each other in the graph. The framework adopted for the implementation of the proposed approach is Apache Spark, useful for loading, managing and manipulating data by means of appropriate Resilient Distributed Datasets (RDD). Moreover, the Alternating Least Square (ALS) machine learning algorithm, a Matrix Factorization algorithm for distributed and parallel computing, is applied. Preliminary obtained results seem to be promising.
Storing Feature Vectors in Relational Image Data Warehouses

An image data warehousing also manipulates image data represented by feature vectors and attributes for similarity search. In this paper, we study the impact of storing feature vectors on the performance of analytical queries extended with a similarity search predicate over images. We consider the management of huge data volumes. Thus, we use Spark as support. Experimental results showed that depending on the query characteristics and the data warehouse design, the difference in performance was up to 86.23%. Based on these results, we propose guidelines for storing feature vectors in relational image data warehouses.
Semantically-Driven Secure Task Execution over Wireless Sensor Networks

The growing adoption of low-cost sensors is raising valid concerns with regards to data, user, computation, and network privacy. In this paper we propose a semantically-driven secure task execution on wireless sensor networks. We rely on blockchain smart contracts and onion routed task execution driven by semantic descriptions to respectively provide role-based access control (RBAC) for query, and support local privacy-preserving task execution. We validate the feasibility of our approach in terms of query time, relative to size of the payload, and number of sensor in the network through NS3 simulations.
Visualisation of Numerical Query Results on Industrial Data Streams

The capability to efficiently handling and analysing data streams in industrial processes and industrial cyber-physical systems (ICPS) is critical for digitalisation and renewal of current manufacturing industry. A key problem within this context is to provide scalable capability to collect, process, analyse, and visualise data streams to support these ICPSs. The status of these systems continuously changes, and analysts must understand and act upon such changes often in real time. Visualisation tools are increasingly used by analysts to get insights from these changes, but inconveniently nowadays, analysts have to store the data from the Industrial Data Stream to a data storage system and then use some visualisation tools to be able to visualise the data to help them understand and act promptly. In this paper, we propose to integrate visualisation and analysis primitives transparently into the query language of the Data Stream Management System (DSMS) and we show a proof of concept by a successful integration of an operator that can execute query-based visualisation methods that support processing of continuous numerical queries over streaming data in industrial analytics applications. We will also show how we are benefiting from the meta data in DSMSs to perform dimensionality reduction in real time to identify a two, three, four, or five-dimensional representation of the numerical query results on the data stream which preserve the salient relationships in the results and how the operator can suggest the most appropriate visualisation of the data to the analyst.
Machine Learning-Based Model Categorization Using Textual and Structural Features

Model Driven Engineering (MDE), where models are the core elements in the entire life cycle from the specification to maintenance phases, is one of the promising techniques to provide abstraction and automation. However, model management is another challenging issue due to the increasing number of models, their size, and their structural complexity. So that the available models should be organized by modelers to be reused and overcome the development of the new and more complex models with less cost and effort. In this direction, many studies are conducted to categorize models automatically. However, most of the studies focus either on the textual data or structural information in the intelligent model management, leading to less precision in the model management activities. Therefore, we utilized a model classification using baseline machine learning approaches on a dataset including 555 Ecore metamodels through hybrid feature vectors including both textual and structural information. In the proposed approach, first, the textual information of each model has been summarized in its elements through text processing as well as the ontology of synonyms within a specific domain. Then, the performances of machine learning classifiers were observed on two different variants of the datasets. The first variant includes only textual features (represented both in TF-IDF and word2vec representations), whereas the second variant consists of the determined structural features and textual features. It was finally concluded that each experimented machine learning algorithm gave more successful prediction performance on the variant containing structural features. The presented model yields promising results for the model classification task with a classification accuracy of 89.16%.
Construction of Online English Corpus Based on Web Crawler Technology

Using 100 random questionnaires, 89 people said they were increasingly dependent on the Internet for information, while the remaining 11 said they were not dependent much. Nowadays, the development of Internet technology is more and more mature, and the scale of the Internet is more and more large. At the same time, with the gradual deepening of global economic integration, English has become one of the indispensable language methods for international communication and cooperation. The development of network technology has been applied more and more widely in the process of English teaching; especially, the construction, research, and practical application of corpus have ushered in a broad development prospect. Based on web crawler technology, this paper focuses on the construction of web English corpus, which lays a foundation for English learning. Experiments show that crawler technology can effectively solve the collection and recognition of big data in English corpus.
A Study on Reinforcement Learning Method for the Deception Behavior : Focusing on Marine Corps Amphibious Demonstrations

Military deception is an action executed to deliberately mislead enemy’s decision by deceiving friendly forces intention. In the lessons learned from war history, deception appears to be a critical factor in the battlefield for successful operations. As training using war-game simulation is growing more important, it is become necessary to implement military deception in war-game model. However, there is no logics or rules proven to be effective for CGF(Computer Generated Forces) to conduct deception behavior automatically. In this study, we investigate methodologies for CGF to learn and conduct military deception using Reinforcement Learning. The key idea of the research is to define a new criterion called a “deception index” which defines how agent learn the action of deception considering both their own combat objectives and deception objectives. We choose Korea Marine Corps Amphibious Demonstrations to show applicability of our methods. The study has an unique contribution as the first research that describes method of implementing deception behavior.
Occlusion Robust Military Vehicle Detection using Two-Stage Part Attention Networks

Detecting partially occluded objects is difficult due to the appearances and shapes of occluders are highly variable. These variabilities lead to challenges of localizing accurate bounding box or classifying objects with visible object parts. To address these problems, we propose a two-stage part-based attention approach for robust object detection under partial occlusion. First, our part attention network(PAN) captures the important object parts and then it is used to generate weighted object features. Based on the weighted features, the re-weighted object features are produced by our reinforced PAN(RPAN). Experiments are performed on our collected military vehicle dataset and synthetic occlusion dataset. Our method outperforms the baselines and demonstrates the robustness of detecting objects under partial occlusion.
MENINGKATKAN KEMAMPUAN MENULIS PUISI BALI MODERN DENGAN MENERAPKAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM ASSISTED INDIVIDUALIZATION (TAI) PADA SISWA KELAS VII H SMP DHARMA WIWEKA DENPASAR TAHUN PELAJARAN 2021/2022”

The ability to write modern Balinese poetry in class VII H SMP Dharma Wiweka Denpasar is still low. To overcome this, the researcher offers to apply the Team Assisted Individualization type of cooperative learning model. The formulation of the problem in this study are: (1) Can the application of the Team Assisted Individualization (TAI) learning model improve the ability to write modern Balinese poetry for students of class VII H SMP Dharma Wiweka Denpasar ? (2) How are the students of class VII H SMP Dharma Wiweka Denpasar responding to the application of the Team Assisted Individualization (TAI) learning model? The application of the Team Assisted Individualization (TAI) learning model can improve the ability to write modern Balinese poetry in class VII H SMP Dharma Wiweka Denpasar . This can be seen from the test results of students in the first cycle obtaining an average score of 76.19 with a total percentage of 71.79%. In the second cycle there was an increase with an average score of 83.14 with a total percentage of 89.74%, besides that an increase also occurred in student responses with an average score of 12.43 in the first cycle to 16.76 in the second cycle from a maximum score of 20 with a very positive category. It is recommended that Balinese language teachers apply the Team Assisted Individualization (TAI) learning model in learning to write modern Balinese poetry, students are advised to improve their abilities by practicing more in writing modern Balinese poetry, for the school the Team Assisted Individualization (TAI) learning model can be used as a learning model. material for consideration to be applied to other lessons. Keywords: Team Assisted Individualization (TAI) model, writing modern Balinese poetry.
THE USE OF TIME TOKEN METHOD IN IMPROVING STUDENTS’ SPEAKING SKILL AT THE SECOND GRADE OF ISLAMIC SENIOR HIGH SCHOOL MAN BANTAENG

This research aims to find out whether or not the implementation time token method is effective to improve the students’ speaking skill in term of fluency and accuracy at the second grade of Islamic senior high school MAN Bantaeng. This research used pre-experimental method. The population of this research consists of the second grade and the sample of the research is XI IIS 2 consists of 16 students at MAN Banteng, year 2021/2022. In collecting the data, the researcher gave a speaking test there was pre-test and post-test, while to analyze the data the researcher used t-test. The result of this researcher showed that the mean score in pre-test for fluency was 1.18 and post-test was 2.9. The mean score in pre-test for accuracy was 1.43 and in post-test was 3. And the total mean score of students’ speaking skill in pre-test was 2.6 and post-test 6. The degree of freedom significance 5% was 2.131 it was higher than the result of the significance difference of the post test that was 10,088. It means that by applying time token method in teaching speaking can help in improve the students’ speaking skill. Thus, concluded that improving students’ speaking skill is significantly better after getting treatment. So the alternative hypothesis is accepted and the null hypothesis is rejected. Therefore, it is proven that time token method can improve the students’ speaking skill.
CSGN: Combined Channel- and Spatial-Wise Dynamic Gating Architecture for Convolutional Neural Networks

The explosive computation and memory requirements of convolutional neural networks (CNNs) hinder their deployment in resource-constrained devices. Because conventional CNNs perform identical parallelized computations even on redundant pixels, the saliency of various features in an image should be reflected for higher energy efficiency and market penetration. This paper proposes a novel channel and spatial gating network (CSGN) for adaptively selecting vital channels and generating spatial-wise execution masks. A CSGN can be characterized as a dynamic channel and a spatial-aware gating module by maximally utilizing opportunistic sparsity. Extensive experiments were conducted on the CIFAR-10 and ImageNet datasets based on ResNet. The results revealed that, with the proposed architecture, the amount of multiply-accumulate (MAC) operations was reduced by 1.97–11.78× and 1.37–13.12× on CIFAR-10 and ImageNet, respectively, with negligible accuracy degradation in the inference stage compared with the baseline architectures.
A Deterministic Approximation to Neural SDEs

Neural Stochastic Differential Equations (NSDEs) model the drift and diffusion functions of a stochastic process as neural networks. While NSDEs are known to make accurate predictions, their uncertainty quantification properties have been remained unexplored so far. We report the empirical finding that obtaining well-calibrated uncertainty estimations from NSDEs is computationally prohibitive. As a remedy, we develop a computationally affordable deterministic scheme which accurately approximates the transition kernel, when dynamics is governed by a NSDE. Our method introduces a bidimensional moment matching algorithm: vertical along the neural net layers and horizontal along the time direction, which benefits from an original combination of effective approximations. Our deterministic approximation of the transition kernel is applicable to both training and prediction. We observe in multiple experiments that the uncertainty calibration quality of our method can be matched by Monte Carlo sampling only after introducing high computational cost. Thanks to the numerical stability of deterministic training, our method also improves prediction accuracy.
SpotFuzzer: Static Instrument and Fuzzing Windows COTs

The security research on Windows has received little attention in the academic circle. Most of the new methods are usually designed for the Linux system and are difficult to transplant to Windows. Fuzzing for Windows programs always suffers from its closed source. Therefore, we need to find an appropriate way to achieve feedback from Windows programs. To our knowledge, there are no stable and scalable static instrumentation tools for Windows yet, and dynamic tools, such as DynamoRIO, have been criticized for their performance. To make matters worse, dynamic instrumentation tools have very limited usage scenarios and are impotent for many system services or large commercial software. In this paper, we proposed SpotInstr, a novel static tool for instrumenting Windows binaries. It is lightweight and can instrument most Windows PE programs in a very short time. At the same time, SpotInstr provides a set of filters, which can be used to select instrumentation points or restrict the target regions. Based on these filters, we propose a novel selective instrumentation method which can speed up both instrumentation and fuzzing. After that, we design a system called SpotFuzzer, which leverages the ability of SpotInstr and can fuzz most Windows binaries. We tested SpotInstr and SpotFuzzer in multiple dimensions to show their superior performance and stability.
Spydobot-AI Based autonomous spider like robot for spying

Autonomous companion robots have shown to be particularly beneficial for gathering information in areas where people are restricted. Controlling them is often a challenge ng feat due to the environment's ambiguity and the nonlinear dynamics of the grounds. Despite the fact that a variety of controller designs are feasible, and some are documented in the literature, it is unknown which designs are best suited for a certain context. In this paper, we attempted to design a robot that can be adapted for usage in any environment by making only skeleton alterations, we designed the controller with integrating Neural network nodes with Q-learning algorithm to regulate movement of the robot using LIDAR samples. With military applications in consideration, we implemented encryptors to send and receive data, and we distributed all dumps to the controller to ensure that we only needed to be connected when delivering data to the owner. As all this requires high processing speed and storage, we recommend using ESP32-S2 for its high clock speed.
Analyzing Information Flow of Hashtag Networks during Elections using Sentiment Analysis and Graph Algorithms

An exponential increase in the usage of social media across the world creates a lot of unstructured data and cross-communication between individuals. These platforms provides opportunity to the political parties to spread their word out. The information is spread using several hashtags in the form of user-generated tagging that facilitates cross-referencing of content. These hashtag-generated networks serve as a huge reservoir of data and if analyzed systematically can help in understanding the agenda-setting of each party and how successful or unsuccessful they are. This in turn helps in predicting the outcome of the election looking from the prism of social media. In the present study, a model is proposed by combining sentiment analysis and graph techniques to look into the trending hashtag networks propagated by political parties using Twitter. The sentiment analysis gives us a sense of inclination of each tweet and thereafter it’s extrapolated onto the hashtag’s user network to get insights as to how the information is diffusing and how one party propagates its favorable hashtag and how the others try to counter it. The major aim of the present work is to find out the intricacies that go on in the social media space before a major election.
Anomaly Detection In Telemetry Data Using Ensemble Machine Learning

This paper explores unsupervised machine learning methods for anomaly detection in telemetry datasets by reviewing and identifying best-automated detection algorithms and methodologies for anomaly detection. There have been various research to identify an effective model to detect anomalies for telemetry data to reduce response time so as to mitigate risks and avoid failures. Traditional algorithms for anomaly detection have trouble identifying attacks throughout the data analysis task. Machine learning approaches, such as supervised, and unsupervised methods for grouping, classification, and regression, appear to be very useful tools for analyzing anomalous behavior. These techniques can identify any anomalous behavior in telemetry data and allow room for research into the real-time analysis. The principal aim of this research is to answer the question "How can we improve on the current machine learning models for anomaly detection in telemetry datasets?". The dataset consists of five Time-Series datasets and is representative of the data with which we are concerned. Five algorithms are applied to these datasets and examined in depth. Then, three unsupervised anomaly definitions are examined.
High Throughput VLSI Architecture for Image Pyramid Generation in Computer Vision

Image Pyramids i.e. set of down-sampled images from high-resolution input is used in computer vision processing pipe to account for unknown distance of objects from Camera. The image pyramid can be set of Octave (down-sampling by 2) and/or Generic scaling (arbitrary downscaling ratio). The prior literature addresses generating all down-scaled images using either input or previous output as an overall scaling architecture and consists of "N" set of independent scalers, which are separately tuned to Octave and generic scaling. The disadvantage of the above approach is higher silicon area and DRAM Bandwidth proportional to number of independent scalars. The paper proposes a novel solution on top of traditional poly-phase filtering which consists of new concepts e.g. Re-scale from previous octave scale architecture, Multi-thread processing with flexible mapping of shared Scalers, Unconventional processing order for 2D scaling without line buffers, shared coefficients and flexible Region of Interest (ROI). The proposed solution is implemented as HW IP with 0.2 mm2 in 16nm process node with 720 Mpix/sec throughput, which is 3.5X lower in the area and 40% lower DRAM bandwidth compared to prior literature.
A Novel Batch Scheduling Algorithm with Digital Simultaneous Looks for Airborne Surveillance Radars

In airborne surveillance radars, priority tracking of maneuvering targets with high g-factors, popularly known as full track mode, is of paramount importance. In a dense target scenario, priority/full tracking of a large number of maneuvering targets, require an efficient radar task scheduler. We propose an improved radar scheduler based on two methods namely, 1) Simultaneous Multi-directional Detection with Phase Spoiling (SMDPS) and 2) Batch Earliest Deadline First Algorithm (BEDF). The proposed scheduler simultaneously utilizes the advantage of transmit beam shaping in Active Electronically Scanned Array and batch processing nature of BEDF. We show that our algorithm drastically improves over the conventional EDF algorithm in terms of total number of priority/full tracks. A unique feature of our algorithm is that maximum number of priority tracks is independent of target density and is only limited by the available processing power. The results are of particular interest in airborne surveillance where functionalities like threat assessment, intercept control etc., are heavily dependent on the efficiency of radar scheduler.
An Approach to Evaluating Subjective Answers using BERT model

The state of art model for language translation, conversion from hand written to digital text, transcription are succeeded in wide range of fields using Natural Language Processing, Artificial Intelligence and Machine Learning (AIML) applications. In present, evaluation of subjective answers are not exercised systematically and graded using computer system. In this work, a mathematical method is proposed for evaluating subjective answers using Bidirectional Encoder Representation Transformers for word embedding and convert the sentence into vector space using pooling method for representing similar sentences. The proposed method evaluates the subjective answers having semantic meaning of answers based on topic Engineering and Medical related questions and answers dataset. It achieves to understand the similarity of different answers which are same semantically. The BERT model is used with machine learning methods to transform the sentence into vector space. The vector space is used to calculate percentage of similarity. The similarity of the sentences with percentage is observed and evaluated.