Reviewerly/exHarmony huggingface dataset:
Congruence of Deep Learning in Medical Image Processing: Future Prospects and Challenges

Machine Learning and deep learning procedures, in particular deep reinforcement neural networks, have quickly become a smart approach for scrutinizing medical signal and image datasets. The perilous problem like Cancer takes place when the cellular reproduction procedure goes out of control when some parts of the human body’s cells arise to spread into contiguous fleshy tissue and divide without discontinuing which may be a clue to death. This fatal cancer is a dangerous disease categorized by undesirable, uncontrolled, and uncoordinated cell division. So early cancer diagnosis can protect a patient. Here, various types of deep learning techniques are applied for optimized feature extraction, normalization or dataset pre-processing (used to eliminate null value, noises, and outlier), data segmentation, accurate classification, and the common description of flow chart is described in Figs. 1 and 7. The survey of deep learning is used for image classification, carotid ultrasound data investigation, cardi tocography, intravascular ultrasound report, lung CT report, brain tumor prediction from the MRI report, object detection, segmentation, breast cancer prediction, ECG (electrocardiogram) signal, EEG (electroencephalogram), PPG signal registration and psoriasis skin disease as well as cancer detection. Concise summaries are delivered of trainings per application zone: pulmonary, musculoskeletal neuro, digital pathology, abdominal, retinal, breast, cardiac. There are various type of deep learning techniques are present to improve accuracy of the medical dataset. Deep reinforcement learning, Recursive neural network, Multilayer perceptron, Recurrent neural network, Boltzmann machine, Convolution neural network are different types of deep learning techniques used to train the image and signal dataset. Generative adversarial network, Auto encoder and deep belief neural network are coming under unsupervised pretrained neural network. Some well known architectural models of convolution neural networks are ResNet (2015), VGGNet (2014), GoogLeNet (2014), ZFNet (2013) is introduced as the visualization concept of the De-convolutional network, AlexNet (2012) and LeNet are basically used to train image dataset, LSTM technique (long short term memory) is used to train signalized dataset and RHSBoost, genetically optimized neural network are used for multiple classification of datasets efficiently. Dimensionality reduction, feature extraction, overfitting, underfitting and normalization problems can be solved using various types of optimization algorithm.
Analyzing Team Performance with Embeddings from Multiparty Dialogues

Good communication is indubitably the foundation of effective teamwork. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomena in which humans synchronize their linguistic choices. This paper examines the problem of predicting team performance from embeddings learned from multiparty dialogues such that teams with similar conflict scores lie close to one another in vector space. Embeddings were extracted from three types of features: 1) dialogue acts 2) sentiment polarity 3) syntactic entrainment. Although all of these features can be used to effectively predict team performance, their utility varies by the teamwork phase. We separate the dialogues of players playing a cooperative game into stages: 1) early (knowledge building) 2) middle (problem-solving) and 3) late (culmination). Unlike syntactic entrainment, both dialogue act and sentiment embeddings are effective for classifying team performance, even during the initial phase. This finding has potential ramifications for the development of conversational agents that facilitate teaming.
Artificial Intelligence Technology Based on Machine Learning Algorithm

This paper first introduces the background and research status of artificial intelligence and machine learning. Secondly, it briefly introduces the classical algorithms of machine learning in theory and puts forward some opinions. Finally, it thinks that deep learning is the research prospect of machine learning, and the development trend of machine learning is prospected.
A measurement framework of crowd intelligence

Purpose The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network. Design/methodology/approach The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions. Findings The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages. Research limitations/implications The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study. Originality/value Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.
Quantifying Human-Perceived Answer Utility in Non-factoid Question Answering

Taking a user-centric approach, we study the features that render an answer to a non-factoid question useful in the eyes of the person who asked that question. An editorial study, where participants assess the usefulness of the answers they received in response to their questions, as well as 12 different aspects associated with the answers, indicates considerable correlation between certain aspects such as relevance, correctness, and completeness with the user-perceived usefulness of answers. Moreover, we investigate the effectiveness of some commonly used answer quality measures, such as ROGUE, BLEU, METEOR, and BERTScore, demonstrating that these measures are limited in their ability to capture the aspects of usefulness and have room for improvement. The question answering dataset created in our work was made publicly available.
Application of 2D resistivity from geoelectrical methods with dipole dipole configuration for identification of land slides in Citeko Village, Cisarua, Bogor District, West Java

Abstract At present, many methods are used to discussed about landslide problems, especially regarding the slip field from the perspective of a one-disciplinary or multidisciplinary approach. One of the approach is using the 2D resistivity geoelectrical method. This research has been carried out to identify landslide and slip fields in Citeko Village, Cisarua District, Bogor Regency using the dipole-dipole resistivity configuration on geoelectrical method. Measurements were made in three passes with 235 metress in length and 5 metress in electrode distance. Data were processed using Res2dinv software, and the results showed that in the study, area lithology of its subsurface rocks was detected, including alluvium and sand layers, weathered material, up to volcanic rocks. Then, there are also clay layers, silt clay layers, and sandstones. The slip fields could be analyzed like a clay layer with resistivity values ranging from 42.3 Ωm −1 to 155 Ωm −1 , and its position was in line 1 and line 2, with a depth of 8.5 metres - 16.4 metres. It also identified in line 3 at the depth of 16.4 metres. Then, based on the interpretation of resistivity 3D modeling with Rockworks software from geoelectric data, it shows that the direction of the landslide tends to be centered towards the Northwest to the West, this is also supported by visual observations in the field.
Multi-Stream Semantics-Guided Dynamic Aggregation Graph Convolution Networks to Extract Overlapping Relations

The existing relation extraction approaches select the relevant partial dependency structures and exhibit the limitation associated with long-distance dependencies. Moreover, due to the repeated use of the irrelevant redundant information and the lack of consideration of the key semantic details, the extraction of relations is relatively complex when the entities overlap. To address this limitation and effectively exploit the relevant information while ignoring irrelevant information, this paper proposes a simple but effective multistream semantics-guided dynamic aggregation graph convolution network (SG-DAGCN) to realize the extraction of overlapping relations. The proposed model constructs the entity relation graphs by enumerating the possible candidates and external auxiliary information and adaptively manages the relevant substructure. Subsequently, this framework models the relational graphs between the entities through a dynamic aggregation graph convolution module and gradually produces the discriminative embedded features and a refined graph through the dynamic aggregation of nodes. The proposed approach can effectively leverage the rich multiscale structural information and capture the long-distance dependencies between overlapping entities in long sentences. The results of the experiments conducted on two typical benchmark datasets show that the proposed model can achieve a high level of performance and outperform other state-of-the-art methods in both qualitative and quantitative aspects.
A general construction for generating pseudorandom sequences using the digit expansion of real functions

In this paper, we propose a general construction for creating statistically random sequences using digit expansions of real functions. We identify different pitfalls and weaknesses in the presented system. A pseudorandom number generator (PRNG) is described based on a modified Riemann-Siegel Z(t) function. It is shown that the output satisfies the statistical requirements of the NIST SP 800-22 randomness suite. We also analyze the resistance of the system against some well-known cryptographic attacks.
Reranking of Responses Using Transfer Learning for a Retrieval-Based Chatbot

This paper presents how to improve retrieval-based open-domain dialogue systems by re-ranking retrieved responses. The paper uses a retrieval based open domain dialogue system implemented previously, namely Iris chatbot as a case study. We investigate two approaches to re-rank the retrieved responses. The first approach trains a re-ranker using machine generated responses that were annotated by human participants through WOCHAT (Workshops and Session Series on Chatbots and Conversational Agents) and its shared-tasks [5, 6]. The second approach uses transfer learning by training the re-ranker on a large dataset from a different domain. We chose the Ubuntu dialogue dataset as the domain. The human evaluation test asked subjects to rank and review three different dialogue systems, the baseline Iris system, the Iris system enhanced with a re-ranker trained on WOCHAT data, and the Iris system enhanced with a re-ranker trained on the Ubuntu data. The Iris system enhanced with a re-ranker trained on WOCHAT data received the highest ratings from the human subjects.
A Design Approach for Mac Unit Using Vedic Multiplier

Machine learning problems have been efficiently solved by using Artificial Neural Networks (ANNs). The realization of neural networks on hardware have been shown to provide more significant advantages. In digital neural networks, the weight-input multiplication is an important step. In this paper, a comparative study between different configurations of Vedic multipliers and traditional array multipliers has been performed and further, the hardware implementation of the MAC unit has been performed using VHDL. MAC unit of ANN requires repetitive use of adders and multipliers. The aim behind the comparison is to obtain an alternative approach for the realization of the MAC unit of the neural network. This paper further proposes a network using the alternative multiplier in place of the normal array multiplier. The circuit implemented in this paper has been dedicated to a given data set. The testing accuracy by the network is achieved keeping in mind the precision of the multiplier.
Text and Image: A new hybrid authentication Scheme

Abstract Nowadays authentication considered the most vital field in term of security aspects because it represents the first line of defense in total process of our assets protection. In order to get access to any data especially when they are private, authentication is required as the first step of protecting accessing our assets. Therefore, many techniques been invented to create appropriate authentication process, for example the traditional textual password technique, the graphical passwords techniques and biometrics techniques. These techniques involved a sequence of procedures all aim to three things (confidentiality against attackers, acceleration of access time, simplification of the created technique). However, the rapid growth of threats for example the shoulder surfing attacks led to invent many techniques concentrated only on the confidentiality aspect and neglected other important aspects like easiness of utilization, as a result; these techniques suffered distinctly the issue of complexity. Many techniques been proposed in order to eliminate the complexity problem without losing their confidentiality purpose, however; most of them did not succeeded. For that reason, this research present a new hybrid technique that combines the maintenance of confidentiality against shoulder surfing attacks and the simplification of utilization at the same time, our technique consist of two simple techniques but effective to solve complexity issue with maintaining the confidentiality aspect, these two techniques are the traditional textual password and the graphical password.
How Surprising! Mirativity, Evidentiality and Abductive Inference

Mirativity is a grammatical category or a linguistic strategy that makes explicit the surprising aspect of a piece of information. Different mirativity strategies appear in different languages. Evidentiality is a grammatical category that explicitly expresses the source of information, i.e. if something has been seen, heard or inferred. Whether mirativity forms part of evidentiality is an open question. An agent makes use of a mirativity marker when she or he expresses something about a surprising fact with respect to her or his background knowledge. But there are different kinds of mirativity markers. Some of them are simple and direct and others, more complex, involve inferential processes. Here, we focus on complex mirativity and we compare it with inferred evidentials and deferred realizations. Our proposal goes beyond the purely linguistic analysis, since we try to interpret mirativity, and its role in interaction, as forming part of or involving a particular kind of inference, namely abduction. Mirativity can be used to express the surprising state that triggers an abductive reasoning but it can also encapsulate its result in such a way that it can be understood in relation to evidentiality. Thus, at least in some cases of complex mirativity, mirativity may convey indication about the source of information. Interesting for studies on dialogue is that they also indicate the degree of commitment of the speakers with the content of their own utterances.
Improving Search Engine Ranking Prediction Based on a New Feature Engineering Tool

Nowadays, companies wishing to get exposure and increase revenues need Internet visibility, and particularly get on the top of Search Engines Results Pages (SERP). Meanwhile, in the last two decades, data science became a vital part of many industries to get quick insights and valuable information, including online marketing and Search Engine Optimization (SEO). With 90% market share worldwide, Google is a search engine using an algorithm to query billions of pages with more than 200 factors that all marketers want to discover. As consequence, some applications are existing to get insights about SEO factors relative to webpages. However, it does not exist a tool able to collect large quantities of URLs and analyze them relative to on-page factors. Therefore, a custom tool "SEOnaozi" was developed for this purpose. This article tries different machine learning algorithms to evaluate the SEO tool and determine how effective it can be to improve SEO analysis. For this purpose, three datasets for a total of 50,000 observations were tested with two scenarios: one using a selection of 8 features, the other with 23 features, including 15 on-pages features. According to the research result, it leads that the tool "SEOnaozi" is efficient because all metrics mostly improved when on-pages features are used by algorithms. Moreover, it was found that Catboost could produce the best results among others to predict an URL to be on Google Top10 SERP.
Expressive Rule Pattern Based Compression with Ranking in Horn Rules on RDF Style KB

With the increasing growth of linked open datasets published in the RDF format, demands for RDF compression has been becoming extremely important. There are a number of structured compression schemes that consider the structural redundancies. A very few researches have done that focuses on compact representation. In this study, we compress the RDF datasets by using different semantic association rules learned from the RDF graphs. From the RDF datasets, our proposed system mines the logically related Horn rules for accomplishing higher compression. The system keeps the triples that match the antecedent part and deletes them while match with the head part of the rules. We have proposed the grammar based pattern system (GBS) to reduce the search space size and faster projection. Moreover, we also have proposed the ranking rules and top-k approach for effectively utilizing all the rules and decreasing the mining time. The experimental result affirms that our proposed system has achieved greater compression compared to the existing RB compression approach.
Local geochemical features of Lower Paleozoic rocks in the area of Duderhof dislocations (North-West of the Russian plate)

Research subject. A geochemical study of Cambrian-Ordovician deposits was carried out within the area of the Duderhof dislocations of the platform cover of the southern frame of the Baltic shield. Materials and methods. The ICP-MS method was used to study 14 samples of sandstones, limestone, phosphorites and black shales, which comprise both weakly dislocated stratified formations (stratified horizons) and strongly deformed formations, as well as secant dome-like and dike-like bodies. Results. Geochemical studies showed that, in the zones of tectonic disturbances, lower Paleozoic deposits are characterized by high concentrations of a number of trace elements, significantly exceeding the Clark values for sedimentary rocks. This applies to such chemical elements, as Be, Sc, V, Co, Ni, Cu, Mo, Pb, Th and U. At the same time, the concentrations of V and U are higher than those in the developed fields of Estonia and Sweden. An analysis of the cambriansands of the sablin formation showed that the eastern (Archean) part of the Baltic shield could not have been the source of food for their formation. The presence of leucoxene in the sands of the Sablinsky formation, a mineral formed by ilmenite under the influence of high temperatures, and the presence of mechanically unstable minerals in dike-like formations, indicate the influence of deep fluids on the sedimentary cover, whose flows moved along fault zones and carried the above-mentioned chemical elements to the near-surface layers of the earth’s crust. All this points to the endogenous rather than exogenous (glacial) nature of secondary structural-material transformations in the area of the Duderhof dislocations, as well as their genetic relationship with deformations in the Baltic-Mezen shear zone.
Automatic Workload Estimation for Software House

Normally, organizations have to estimate the workload relying on limited resources. An appropriate estimation method can improve workforce optimization. In the software house, workload categorization and estimation can be acquired from the information technology management. Nevertheless, there are several factors such as work priority and specific goals that affect the workload level. General workload management spends a long time and decreases task management quality. Therefore, this study applies machine learning, Naïve Bayes, to estimate the workload automatically. This classification method increases the accuracy of workload estimation, along with reducing the time consumption for the whole system.
Tradeoff Attacks on Symmetric Ciphers

Tradeoff attacks on symmetric ciphers can be considered as the generalization of the exhaustive search. Their main objective is reducing the time complexity by exploiting the memory after preparing very large tables at a cost of exhaustively searching all the space during the precomputation phase. It is possible to utilize data (plaintext/ciphertext pairs) in some cases like the internal state recovery attacks for stream ciphers to speed up further both online and offline phases. However, how to take advantage of data in a tradeoff attack against block ciphers for single key recovery cases is still unknown. We briefly assess the state of art of tradeoff attacks on symmetric ciphers, introduce some open problems and discuss the security criterion on state sizes. We discuss the strict lower bound for the internal state size of keystream generators and propose more practical and fair bound along with our reasoning. The adoption of our new criterion can break a fresh ground in boosting the security analysis of small keystream generators and in designing ultra-lightweight stream ciphers with short internal states for their usage in specially low source devices such as IoT devices, wireless sensors or RFID tags.