Reviewerly/exHarmony huggingface dataset:
Data Tamper Detection from NoSQL Database in Forensic Environment

The growth of service sector is increasing the usage of digital applications worldwide. These digital applications are making use of database to store the sensitive and secret information. As the database has distributed over the internet, cybercrime attackers may tamper the database to attack on such sensitive and confidential information. In such scenario, maintaining the integrity of database is a big challenge. Database tampering will change the database state by any data manipulation operation like insert, update or delete. Tamper detection techniques are useful for the detection of such data tampering which play an important role in database forensic investigation process. Use of NoSQL database has been attracted by big data requirements. Previous research work has limited to tamper detection in relational database and very less work has been found in NoSQL database. So there is a need to propose a mechanism to detect the tampering of NoSQL database systems. Whereas this article proposes an idea of tamper detection in NoSQL database such as MongoDB and Cassandra, which are widely used document-oriented and column-based NoSQL database respectively. This research work has proposed tamper detection technique which works in forensic environment to give more relevant outcome on data tampering and distinguish between suspicious and genuine tampering.
Parsing Argumentative Structure in English-as-Foreign-Language Essays.

This paper presents a study on parsing the argumentative structure in English-as-foreign-language (EFL) essays, which are inherently noisy. The parsing process consists of two steps, linking related sentences and then labelling their relations. We experiment with several deep learning architectures to address each task independently. In the sentence linking task, a biaffine model performed the best. In the relation labelling task, a fine-tuned BERT model performed the best. Two sentence encoders are employed, and we observed that non-fine-tuning models generally performed better when using Sentence-BERT as opposed to BERT encoder. We trained our models using two types of parallel texts: original noisy EFL essays and those improved by annotators, then evaluate them on the original essays. The experiment shows that an end-to-end in-domain system achieved an accuracy of .341. On the other hand, the cross-domain system achieved 94% performance of the in-domain system. This signals that well-written texts can also be useful to train argument mining system for noisy texts.
A Modified CNN-Based Face Recognition System

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%.
Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language

Online misogyny has become an increasing worry for Arab women who experience gender-based online abuse on a daily basis. Misogyny automatic detection systems can assist in the prohibition of anti-women Arabic toxic content. Developing such systems is hindered by the lack of the Arabic misogyny benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset for Arabic misogyny. We further provide a detailed review of the dataset creation and annotation phases. The consistency of the annotations for the proposed dataset was emphasized through inter-rater agreement evaluation measures. Moreover, Let-Mi was used as an evaluation dataset through binary/multi-/target classification tasks conducted by several state-of-the-art machine learning systems along with Multi-Task Learning (MTL) configuration. The obtained results indicated that the performances achieved by the used systems are consistent with state-of-the-art results for languages other than Arabic, while employing MTL improved the performance of the misogyny/target classification tasks.
Speaker Recognition Systems in the Last Decade – A Survey

Speaker Recognition Defined by the process of recognizing a person by his\her voice through specific features that extract from his\her voice signal.An Automatic Speaker recognition (ASP) is a biometric authentication system.In the last decade, many advances in the speaker recognition field have been attained, along with many techniques in feature extraction and modeling phases.In this paper, we present an overview of the most recent works in ASP technology.The study makes an effort to discuss several modeling ASP techniques like Gaussian Mixture Model GMM, Vector Quantization (VQ), and Clustering Algorithms.Also, several feature extraction techniques like Linear Predictive Coding (LPC) and Mel frequency cepstral coefficients (MFCC) are examined.Finally, as a result of this study, we found MFCC and GMM methods could be considered as the most successful techniques in the field of speaker recognition so far.
Automatic Topics Segmentation for News Video by Clustering of Histogram of Orientation Gradients Faces

TV stream is a major source of multimedia data. The proposed method aims to enable a good exploitation of this source of video by multimedia services social community, and video-sharing platforms In this work, we propose an approach to the automatic topics segmentation of news video. The originality of the approach is the use of Clustering of Histogram of Orientation Gradients (HOG) faces as prior knowledge. This knowledge is modeled as images which governs the structuring of TV stream content. This structuring is carried out on two levels. The first consists in the identification of anchorperson by Single-Linkage Clustering of HOG faces. The second level aims to identify the topics of news program due to the large audience because of the pertinent information they contain. Experiments comparing the proposed technique to similar works were carried out on the TREC Video Retrieval Evaluation (TRECVID) 2003 database. The results show significant improvements to TV news structuring exceeding 96 %.
Robot motion planning benchmarking and optimization through motion planning pipeline

Motion planning algorithms have been designed with adaptability to different problems. However, how to choose a suitable planner for a scene has always been a question worth exploring. This paper aims to find a suitable motion planner under two different scenes and three different queries. The work lies in optimization of sampling-based motion planning methods through Motion Planning Pipeline and Planning Request Adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking, which determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, although the planning time of the algorithm is longer, the generated path quality is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, the performance of the two optimization methods may not be as good as the original algorithm.
A Novel Scheme for Medical Image Compression Using Huffman and DCT Techniques

Image compression is one of the data compression applications in which we convert the original data into a few bits. In image compression, we can simply preserve the data needed by removing unwanted data to be proficient to record or refer data in a functioning form. Hence, the image compression reduces the communication time and increases the communication speed. We mainly use lossy and lossless techniques to remove this type of problem. There is no data loss when we compress images with a lossless image compression technique while some of the unnecessary data losses in lossy image compression technique. By using these processes, we can reduce the data size, which we can save more data in less memory. Here, we have done the uses of Huffman and DCT techniques for image compression.
A Formation Control Strategy of Quadrotor UAV Study Based on MIQP Cluster Track Method

In this paper, obstacle avoidance and obstacle avoidance trajectories of four UAVs are calculated based on the four-rotor UAV cluster planning theory based on MIQP (mixed integral quadratic planning).Articles from QP (quadratic programming), MIQP theory is introduced to obtain, in turn, this paper introduces the single frame four rotor unmanned aerial vehicle (uav) tracks, single frame four rotor unmanned aerial vehicle (uav) obstacle avoidance, double unmanned aerial vehicle (uav) obstacle avoidance path, the obstacle avoidance path between pairs of unmanned aerial vehicle (uav) machine, as well as multiple unmanned aerial vehicle (uav) between machine and calculation theory of obstacle avoidance algorithm verification, verify the algorithm validity in four rotor unmanned aerial vehicle (uav) trajectory planning problems. Based on the default physical parameters of common quadrotor assembly configuration in PIXHAWK flight control system, this paper ensures the effectiveness of cluster trajectory and lays a foundation for the next real flight and larger cluster control..
Automating defense against adversarial attacks: discovery of vulnerabilities and application of multi-INT imagery to protect deployed models

Image classification is a common step in image recognition for machine learning in overhead applications. When applying popular model architectures like MobileNetV2, known vulnerabilities expose the model to counter-attacks, either mislabeling a known class or altering box location. This work proposes an automated approach to defend these models. We evaluate the use of multi-spectral images to combat adversarial attacks. The original contribution demonstrates the attack, proposes a remedy, and automates some key outcomes for protecting the model’s predictions against adversaries. Similar to defending cyber-networks, we combine techniques from both offensive (“red team”) and defensive (“blue team”) approaches, thus generating a hybrid protective outcome (“green team”). For machine learning, we demonstrate these methods with 3-color channels plus infrared. The outcome uncovers vulnerabilities and corrects them with supplemental data inputs commonly found in overhead cases particularly.
Fine-grained image classification algorithm based on Attention Self-supervision

With the rapid development of artificial intelligence, the purpose of image classification is not only to identify the major categories of objects, but also to classify the images of the same category into more detailed subcategories. In order to effectively discriminate small differences between regions, a fine-grained classification algorithm was proposed based on Attention Self-supervision (ASS). Firstly, the ResNet-152 pre-trained model was applied to extract the global image features and to forecast the attention maps with Depthwise Separable convolution (DS conv). Subsequently, the Weakly Supervised Data Augmentation Network (WS-DAN) was applied to feed the augmented image back into the network and enhance the generalization ability of the network to prevent overfitting. Finally, linear fusion of attention features in ASS network was further performed to improve the accuracy of the classification. The experimental results show that the method achieves 89.6% accuracy rate on CUB-200-2011 dataset.
Sketch Based Thangka Image Retrieval

Thangka art is a cultural representative of Tibet of China and one of the Intangible Cultural Heritage recognized by the United Nations. It's a unique art form of painting in Tibetan culture. Thangka images contain rich historical, cultural and religious information. There is little research about Thangka in the field of image retrieval and content recognition. In this paper, we propose a Thangka-sketch image data set, and study the matching rule between Thangka image and sketch by using deep learning technology based on machine vision. In the last of this paper, we show the result of our Thangka image retrieval.
Analysis Of Risk Management In Development SMK Stikes Rajawali Bandung (Case Study: Well Foundation)

The SMK STIKES Rajawali Bandung project is a unique project because &#x0D; it has a steep terrain location that creates various risks, so a risk analysis &#x0D; is needed. The identification in this study was 32 risks. This research &#x0D; uses qualitative and quantitative methods with the stages of identifying &#x0D; possible risks, assessing risks, knowing the causes, and ways of dealing &#x0D; with dominant risks if they occur. The results of the study of the 32 risks &#x0D; consisted of 3 very high risks, 2 high risks, 23 medium risks and 4 low &#x0D; risks. The biggest risk comes from the external risk that can be detected, &#x0D; namely the risk of rain with a score of 20. Therefore, the risk of rain &#x0D; requires ideal treatment using a dewatering pump.
Forecast and Analysis of Substation Project Investment Based on Multi-factor Matching

Power grid construction projects are the top priority of the energy Internet. Investing limited investment in construction projects that can best improve the economy and reliability of the power grid is the key to optimize investment decision-making and improve investment efficiency. Based on the dynamic investment analysis of the final accounts of 110 kV substations in various regions, a substation project investment forecast model based on multi-factor matching is constructed in this paper. The substation project investment forecast and cost control is realized, and the investment level of substations in different regions is analyzed. The calculation result shows that this method can realize the accurate analysis and calculation of substation investment to strengthen the lean management of substation projects and improve the company's investment decision-making mechanism.
MULTISCALE RETINEX COLOR RESTORATION WITH ADAPTIVE GAMMA CORRECTION FOR FOGGY IMAGE ENHANCEMENT

Typically, the image enhancement of foggy images based on multiscale retinex color restoration (MSRCR) algorithms results in dim images with halo artifacts. This paper presents a foggy image enhancement algorithm that combines MSRCR with adaptive gamma correction. First of all, multiscale Gaussian functions are used to extract the illumination components as per the MSRCR processing of foggy images. Then, an adaptive gamma function is constructed and the distribution characteristics of the illumination components are used to adaptively adjust the gamma function's parameters. The adaptive gamma function can now be used to correct the brightness in the hue, saturation, value (HSV) color space, with the brightness value of over-illuminated areas being suppressed and the brightness value of areas that are too dim being increased. This results in a fused image that can be converted into an RGB color image where the fogginess has been minimized. Experimental results show that the proposed method eliminates the dimness and halo phenomena associated with the basic MSRCR algorithm. In comparison to other foggy image enhancement algorithms the proposed algorithm generally performs better in terms of dehazing and brightness enhancement and is especially effective at removing noise (between 53% and 89% better than the next best-performing algorithm).
EDIOne@LT-EDI-EACL2021: Pre-trained Transformers with Convolutional Neural Networks for Hope Speech Detection.

Hope is an essential aspect of mental health stability and recovery in every individual in this fast-changing world. Any tools and methods developed for detection, analysis, and generation of hope speech will be beneficial. In this paper, we propose a model on hope-speech detection to automatically detect web content that may play a positive role in diffusing hostility on social media. We perform the experiments by taking advantage of pre-processing and transfer-learning models. We observed that the pre-trained multilingual-BERT model with convolution neural networks gave the best results. Our model ranked first, third, and fourth ranks on English, Malayalam-English, and Tamil-English code-mixed datasets.
Tamil Lyrics Corpus: Analysis and Experiments

In this paper, we present a new Tamil lyrics corpus extracted from Tamil movies captured across a range of 65 years (1954 to 2019). We present a detailed corpus analysis showing the nature of Tamil lyrics with respect to lyricists and the year which it was written. We also present similar- ity score across different lyricists based on their song lyrics. We present experi- mental results based on the SOTA BERT Tamil models to identify the lyricists of a song. Finally, we present future research directions encouraging researchers to pur- sue Tamil NLP research.
Why are the batteries in the microwave?: Use of semantic information under uncertainty in a search task

Abstract A major problem in human cognition is to understand how newly acquired information and long-standing beliefs about the environment combine to make decisions and plan behaviors. Over-dependence on long-standing beliefs may be a significant source of suboptimal decision-making in unusual circumstances. While the contribution of long-standing beliefs about the environment to search in real-world scenes is well-studied, less is known about how new evidence informs search decisions, and it is unclear whether the two sources of information are used together optimally to guide search. The present study expanded on the literature on semantic guidance in visual search by modeling a Bayesian ideal observer’s use of long-standing semantic beliefs and recent experience in an active search task. The ability to adjust expectations to the task environment was simulated using the Bayesian ideal observer, and subjects’ performance was compared to ideal observers that depended on prior knowledge and recent experience to varying degrees. Target locations were either congruent with scene semantics, incongruent with what would be expected from scene semantics, or random. Half of the subjects were able to learn to search for the target in incongruent locations over repeated experimental sessions when it was optimal to do so. These results suggest that searchers can learn to prioritize recent experience over knowledge of scenes in a near-optimal fashion when it is beneficial to do so, as long as the evidence from recent experience was learnable.
‘We’re Being Tracked at All Times’: Student Perspectives of Their Privacy in Relation to Learning Analytics in Higher Education

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Blockchains com Hyperledger: conceitos, instalação, configuração e uso

This book presents the texts of the short courses of the 18th Regional School of Computer Networks and Distributed Systems (ERRC 2020), held virtually on 25-27 November 2020. The short courses of ERRC aim to introduce emerging and relevant themes in the areas of Computer Networks, Distributed Systems and Information Security for professionals, graduate students, and undergraduate students.
Learning Complexity of Simulated Annealing.

Simulated annealing is an effective and general means of optimization. It is in fact inspired by metallurgy, where the temperature of a material determines its behavior in thermodynamics. Likewise, in simulated annealing, the actions that the algorithm takes depend entirely on the value of a variable which captures the notion of temperature. Typically, simulated annealing starts with a high temperature, which makes the algorithm pretty unpredictable, and gradually cools the temperature down to become more stable. A key component that plays a crucial role in the performance of simulated annealing is the criteria under which the temperature changes namely, the cooling schedule. Motivated by this, we study the following question in this work: Given enough samples to the instances of a specific class of optimization problems, can we design optimal (or approximately optimal) cooling schedules that minimize the runtime or maximize the success rate of the algorithm on average when the underlying problem is drawn uniformly at random from the same class? We provide positive results both in terms of sample complexity and simulation complexity. For sample complexity, we show that $\tilde O(\sqrt{m})$ samples suffice to find an approximately optimal cooling schedule of length $m$. We complement this result by giving a lower bound of $\tilde \Omega(m^{1/3})$ on the sample complexity of any learning algorithm that provides an almost optimal cooling schedule. These results are general and rely on no assumption. For simulation complexity, however, we make additional assumptions to measure the success rate of an algorithm. To this end, we introduce the monotone stationary graph that models the performance of simulated annealing. Based on this model, we present polynomial time algorithms with provable guarantees for the learning problem.
Utopian futures for sexuality, aging, and design

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Geological Characteristics and Prospecting Criteria of Senji Molybdenum Ore Body in Urad Houqi, Inner Mongolia, China

Geological Characteristics and Prospecting Indicators of Senji MolybdenumOre Body in Urad Houqi, Inner Mongolia Based on geological mapping, soil geochemical survey, surface trough exploration and deep drilling verification and control, the Senji Molybdenum deposit was discovered.Geological characteristics of the molybdenum ore body are described.The host lithology of molybdenum ore body is mainly in the cataclastic carbonated black plagioclase gneiss of the second rock member of the Paleoproterozoic Baoyintu Group, followed by Early Carboniferous monzogranite.The genetic type of the deposit is preliminarily deduced to be a hydrothermal filling vein-type molybdenum deposit.The significance of this paper is to summarize the prospecting marks of the molybdenum ore body in this area, so as to further guide the exploration work in this area, and at the same time provide a useful reference for the exploration work of similar deposits in other areas.