Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial-Temporal Attention Network for Malware Detection Using Micro-architecture Features

Malware detection is an imperative topic in computer security, since an evolutional malware will cause serious damage to computer system and user privacy information security. In recent years, some researchers began to utilize low-level hardware micro-architecture features to detect malware, because these micro-architecture features are difficult for malware evasion. However, these methods always adopt a long sample length and can hardly identify non-signature malware. This situation will inevitably affect the detection efficiency and effectiveness. To solve the above problems, we first select system call instruction as a trigger point to extract low-level features for avoiding blindly collecting unrelated data continuously. Specifically, we use the General-Purpose Registers (GPRs) as features for malware detection. Each register has specific functions and changes of its content contain the action information which thus can be used to detect illegal behaviours. To improve detection efficiency, we then propose a resampling method to well present the spatial and temporal properties of GPRs. Finally, a novel deep learning model is designed to highlight correlations among GPRs for accurate malware detection. Experimental results achieved 99% of Accuracy and zero False Positive rate (FPr) using only a short sample length and can also identify non-signature malware.
A Crowdsourcing Based Human-in-the-Loop Framework for Denoising UUs in Relation Extraction Tasks

In relation extraction tasks, distant supervision methods expand dataset by aligning entity pairs in different knowledge bases and completing the relations between two entities. However, these methods ignore the fact that sentences labels generated by distant supervision methods with high confidence are often incorrect in the real world called Unknown Unknowns (UUs). To deal with this challenge, we propose a crowdsourcing based human-in-the-loop denoising framework which iteratively discovers UUs and corrects them by crowdsourcing to better extract relations. During each epoch of iterations, we choose one sentence bag and repeat two steps: Firstly, attention based Long Short-Term Memory network is applied as a selector to discover potential UUs. Secondly, these UUs are annotated by crowdsourcing with two answer collecting strategies and fed back into selector as positive samples. Until the accuracy of selector reaches a threshold, all annotated samples are added into relation classifier as cleaned train set and framework moves on to next epoch with new sentence bags. The experiments on the New York Times dataset and analysis of potential UUs demonstrate that our framework denoise the dataset and outperforms all the baselines on distant supervision relation extraction tasks.
DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures

BackgroundThere has been increasing interest in the use of deep neural networks for de novo design of molecules with desired properties. A common approach is to train a generative model on SMILES strings and then use this to generate SMILES strings for molecules with a desired property. Unfortunately, these SMILES strings are often not syntactically valid due to elements of SMILES syntax that must occur in pairs.ResultsWe describe a SMILES-like syntax called DeepSMILES that addresses two of the main reasons for invalid syntax when using a probabilistic model to generate SMILES strings. The DeepSMILES syntax avoids the problem of unbalanced parentheses by only using close parentheses, where the number of parentheses indicates the branch length. In addition, DeepSMILES avoids the problem of pairing ring closure symbols by using only a single symbol at the ring closing location, where the symbol indicates the ring size. We show that this syntax can be interconverted to/from SMILES with string processing without any loss of information, including stereo configuration.ConclusionWe believe that DeepSMILES will be useful, not just for those using SMILES in deep neural networks, but also for other computational methods that use SMILES as the basis for generating molecular structures such as genetic algorithms.
Enhanced Matching Network for Multi-turn Response Selection in Retrieval-Based Chatbots

Semantic representation and dependency information are of great importance for matching a response with its multi-turn context. In this paper, we propose an enhanced matching network (EMN) to enhance the matching ability for the multi-turn response selection system in terms of both constructing semantic representation and extracting dependency information. First, the commonly used recurrent neural network (RNN) is replaced with gated convolutional neural network (GCNN) in the matching network to construct more expressive semantic representations of sentences. Second, local inference modeling and inference composition in the enhanced sequential inference model (ESIM) are utilized here to capture enhanced interactive information between the response and each utterance in the context. Finally, EMN is based on a similar multi-turn structure to sequential matching network (SMN) for extracting turns’ dependency information in the chronological order. We furthermore propose a combined model (EMN-SMN) to integrate SMN into EMN for distilling more important dependency information from sentence pairs. Experiments are carried out on Ubuntu Corpus and Douban Conversation Corpus. The results show that EMN can outperform the state-of-the-art methods and the combined model can further improve overall performance.
Declarative Algorithm for Checking Wrong Indirect Relationships of Process Model Containing Non-Free Choice

In the presence of a large number of activities that occur in the company, recording activities are needed to obtain reports on the results of the process. The set of recorded activities is an event log and can be modeled by process discovery to make analysis easier. Out of all existing relationships, Non-Free Choice is a challenging relationship for process discovery algorithms, because Non-Free Choice contains indirect relationships. However, the obtained Non-Free Choice by existing algorithms is a graphical model, so it cannot be used to detect processes containing wrong indirect relationships. A wrong indirect relationship is an indirect relationship that is not fit with the obtained Non-Free Choice. This study proposes a method, namely declarative algorithm for checking indirect relationships, to detect wrong indirect relationships in processes by creating rules of a process model in the form of Linear Temporal Logic (LTL). Experiments prove that this declarative algorithm can detect wrong indirect relationships automatically by using proposed LTL rules deriving from a process model containing Non-Free Choice. The evaluation verifies that the accuracy of obtained wrong indirect relationships is 100%.
Speech Emotion Recognition With Early Visual Cross-modal Enhancement Using Spiking Neural Networks

Speech emotion recognition (SER) is an important part of affective computing and signal processing research areas. A number of approaches, especially deep learning techniques, have achieved promising results on SER. However, there are still challenges in translating temporal and dynamic changes in emotions through speech. Spiking Neural Networks (SNN) have demonstrated as a promising approach in machine learning and pattern recognition tasks such as handwriting and facial expression recognition. In this paper, we investigate the use of SNNs for SER tasks and more importantly we propose a new cross-modal enhancement approach. This method is inspired by the auditory information processing in the brain where auditory information is preceded, enhanced and predicted by a visual processing in multisensory audio-visual processing. We have conducted experiments on two datasets to compare our approach with the state-of-the-art SER techniques in both uni-modal and multi-modal aspects. The results have demonstrated that SNNs can be an ideal candidate for modeling temporal relationships in speech features and our cross-modal approach can significantly improve the accuracy of SER.
Fuzzy logic and Genetic Algorithm based Text Classification Twitter

Social media are a modern web-based application for communication and interaction between humans through audio messages, written messages, and video messages. These devices build and activate living communities around the world. People share their interests and activities with these Applications. Twitter is a social media site, where people communicate through tweets. A service that enables users to communicate in touch through the exchange of frequent tweets and quick. People publish their tweets on their profile and send their followers to express their thoughts and opinions about events in this world. It is crucial to study and categorize these tweets. This work is based on fuzzy logic and genetic algorithm to solve the problem of text classification based on the relevance degree. The Inputs for this classification system are a set of features extracted from a tweet, and the output of this system is a decision of classification for a tweet, which is a degree of relevance for each tweet to an appointed event where the degree of relevance to the desired event iftweet irrelevant or relevant. The results are compared with a method of keyword search and fuzzy logic, which is based on the method incremental rate and correction rate. In the incremental rate, the proposed system is able to extract tweets more than this method, wherein dataset 1, the number of the tweets that are extracted by the proposed system is 160 tweets, but the number of the tweets that extracted by the other one are 98 and141. The correction rate of the proposed system is (98.75), but the correction rates of this method are (97.9) and (95.7).
Performing item-based recommendation for mining multi-source big data by considering various weighting parameters

In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.
Characteristics of liposomes derived from egg yolk

Abstract Liposomes are nanocapsules successfully applied in pharmacy and medicine. Their usage in the food industry could be increased by the development of alternative, cost-efficient lecithin materials. This work is a continuation of the previous two papers describing five different extractions of egg yolk lecithins and the preassessment of their usefulness for liposome formation. Physicochemical properties of extracts differed due to distinct composition. The aim of this research was to further characterise the extracts-based liposomes, especially in terms of nanomechanical properties and structural diversity. Five previously described extracts were used for liposomes preparation employing Bangham technique. Vesicles were analysed with the use of dynamic light scattering, flow cytometry, and atomic force microscopy. The results were tested for correlation with the composition of the extracts. It was proved that the chemical composition of the shell-forming material determined the size, structure, stability, and mechanical properties of the vesicles. The observed effects were found to result not only from differences in the content of major components, i.e. phospholipids, acylglycerols, and cholesterol, but also in the relative proportions. Minor constituents, i.e. tocopherols and carotenoids, were also found to be of significance. Strong correlations between size and Zeta potential of the vesicles with the content of carotenoids were determined. Graphical Abstract
The Effect of Suspension Properties on Shear Yield Stress and Compressibility of the Suspension of Copper Flotation Tailings

The performance of modern slurry dewatering equipment is essentially dependent on the efficiency of compression. Shear yield stress as an index of flowability and compressibility is affected by suspension properties, while the presence of reagents may be significant. In this work, the effect of fine fraction, pH, flocculant, and collector concentration on the shear yield stress of copper flotation tailings at concentrators placed at Sarcheshmeh and Shahrebabak (Iran) were examined. Slump and settling tests were utilized to investigate the shear yield stress and compressibility of suspension. A pilot scale column was also employed to investigate the compressibility of suspension. Results indicated that shear yield stress increase, while compressibility decrease with flocculant concentration and fine fraction. It was found that the shear yield stress steadily increased with pH. Furthermore, in the presence of collectors the shear yield stress was reduced, while the compressibility enhanced.
MENSCHENGESTÜTZTE KÜNSTLICHE INTELLIGENZ

Nach den 1950er und 1970er Jahren gibt es zurzeit wieder einen Hype um das Thema Künstliche Intelligenz (KI). Diesmal scheint KI sogar das Potenzial zu haben, durch Anwendungen in Industrie, Konsumwelt, Politik und Verwaltung spürbare Auswirkungen im Alltagsleben vieler Menschen zu zeigen. Diese seit spätestens 2016 sichtbare Konjunktur von KI geht weitestgehend auf die Erfolge eines bestimmten Ansatzes innerhalb des heterogenen Feldes der KI zurück: auf deep learning (DL), ein auf simulierten künstlichen neuronalen Netzwerken (KNN) beruhendem Verfahren im Bereich des maschinellen Lernens.1 Einige bisher als schwierig geltende informatische Probleme können mit diesem Verfahren nun automatisiert werden, darunter Objekterkennung in Bildern, Transkription gesprochener Sprache, Identifikation von Mustern und Korrelationen in großen Datensätzen. Während der Durchbruch von DL oft als ‹Revolution› für Industrie, Wissenschaft, Politik und Gesellschaft gehandelt wird, betont die medienwissenschaftliche Debatte, dass es sich hierbei nur um eine momentane, vor allem ökonomische, Vormachtstellung eines von mehreren Ansätzen innerhalb der KI-Forschung handelt. So folgt DL einem statistischen, auf der Aggregation von Erfahrungswissen basierenden Ansatz, der etwa dem Paradigma der symbolischen KI, das Intelligenzvermögen als Zeichenkettenmanipulationsvermögen versteht,2 schon seit Alan Turing gegenübersteht. Andreas Sudmann führt aus, dass es zur aktuellen Dominanz der KNN-basierten Künstlichen Intelligenzen vor allem durch eine medientechnologische Revolution gekommen sei, die von der Entwicklung des parallelen Rechnens auf graphical processing units (GPUs) angestoßen wurde.3 Während damit vor allem die Rolle der Hardware betont wird, möchte ich im vorliegenden Artikel zeigen, dass der aktuelle Erfolg von DL auch eine soziale und medienkulturelle Seite hat. Ich werde argumentieren, dass der Durchbruch von DL einen grundlegenden soziotechnischen Strukturwandel forderte: DL-basierte Künstliche Intelligenzen beruhen heute auf Strukturen 1 Vgl. Yann LeCun, Yoshua Bengio, Geoffrey Hinton: Deep Learning, in: Nature, Vol. 521, 2015, 436 – 444; Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning, Cambridge, London 2016. 2 Vgl. John Haugeland (Hg.): Mind Design, Cambridge 1981. 3 Vgl. Andreas Sudmann, Christoph Engemann: Einleitung, in: dies. (Hg.): Machine Learning. Medien, Infrastrukturen und Technologien der Künstlichen Intelligenz, Bielefeld 2018, 9 – 36. MENSCHENGESTÜTZTE KÜNSTLICHE INTELLIGENZ —
Telmatogeton Schiner, 1866 (Chironomidae: Diptera), a newly recorded genus of chironomid larva for the Egyptian Mediterranean Fauna

Abstract Sampling of fouling organisms carried out during January 2019 at Al-Max (west of the city of Alexandria) on the Mediterranean coast of Egypt revealed the presence of larval populations belonging to the genus Telmatogeton (Chironomidae), which is reported for the first time from the Mediterranean coast of Egypt. The larvae were found among clumps of fine filamentous green algae (Enteromorpha compressa). A description of the larva with taxonomic remarks and comments on its ecology and geographical distribution are given.
Determinants of voter turnout in Nsukka Council of Enugu State, South Eastern Nigeria

Abstract Voting is becoming of significance in Nigeria, as in many other countries in Africa. Although Nigerian electoral politics has attracted full attention from scholars, there is little research on the factors that determine voter turnout in the country at the local level, especially the South-East geopolitical zone (GPZ). This paper is a stepwise logistic regression analysis of the determinants of voting in Nsukka council in Enugu State, South-East GPZ of Nigeria. The results show that age (0.230), education (0.532), marital status (1.355), political trust (1.309) and partisanship (˗0.570) are significant predictors of voter turnout. The effect of age, education, marital status and political trust on voting is positive and statistically significant, but partisanship has a statistically significant negative relationship with voting (p<0.01). The paper highlights the importance of local level geographical differentials in the factors influencing voting in Nigeria.
Dislocated quasi cone b-metric space over Banach algebra and contraction principles with application to functional equations

Abstract In this paper we introduce dislocated and dislocated quasi version of a cone b-metric space over a Banach algebra as well as weak semi α-admissible and α-identical pair of mappings and prove common fixed point theorems for a pair of α-identical and weak α-admissible mappings in the aforesaid spaces. Our results are supported with suitable examples and an application to a system of m-tupled functional equations.
Functional Foods for elderly people: new paths for multi “functional” agriculture

Abstract Nowadays a growing interest in how agritourism could be able to promote alternative nutrition stands out. The increase in the number of elderly has determined a desire for quality of life improvement; consequently, more attention has been paid to the role of food and their contents. A diet comprising Functional Foods could help not only to improve life quality of an aging population, as these foods are potentially capable to improve health and well-being; but they could also reduce health risks or delay the onset of serious pathologies. Moreover, scientific evidence of the correlation between diet and health for the elderly population, having a sedentary lifestyle and even a constant increase in healthcare costs, have addressed the interest in healthier food products. The aim of this paper was to design new food-based paths for agritourism farms that could promote health, nutritional and cultural values. To define these paths, a two steps analysis was carried out: (1) opinion collection from elderly and agritourism operators and (2) definition of a logical framework. Results highlighted that differentiating what is offered could increase health levels of elderly and create new multi “functional” shapes for agritourism farms aimed at both offering local and Functional Foods for elderly and at spreading social-tradition values.
A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC is aimed at providing connectivity to tens of billions of sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resources. Nonorthogonal multiple access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing-based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article is aimed at providing an overview of the current state-of-art research work in various compressive sensing-based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby providing useful insights for researchers to make further contributions in NOMA using compressive sensing techniques. Nonorthogonal CDMA massive connectivity grant free medium access compressive sensing multiuser detection
Forecasting the Sales of Console Games for the Italian Market

Abstract In videogames industry, time series analysis can be very useful in determining the general evolution and behaviour of the market dynamics. These methods are applicable to any time series forecasting problem, regardless of the application sector. This article discusses time series approaches to forecast the sales of console games for the Italian market. In particular two univariate techniques were evaluated, exponential smoothing and the SARIMA technique. The aim is to exploit the capabilities of these statistical methods in order to have a comparison of the results and to choose the most accurate model through an ex-post evaluation. Using monthly time-series data from November 2005 to September 2017, the selection of the most suitable model was indicated by the smallest value of the measures of accuracy (MAPE, sMAPE, RMSE) for the out-of-sample observations regarding the period October 2017-September 2018. The implementation of the models was done using Forecast PRO and Gretl. The time series involved is related to the sales regarding the first party manufacturers of consoles and handhelds (Microsoft, Sony and Nintendo).
Wer kann denn schon ja sagen?

Abstract The article explores German discourse particles (DiPs) in rhetorical wh-questions (wh-RQs). While schon (roughly ‘unexpectedly’) only marks rhetorical wh-questions, denn (roughly ‘I wonder’) marks contextually arising information-seeking or rhetorical Questions under Discussion (QuDs), with or without schon. Since ja (roughly ‘unquestionably’) marks shared information, it is incompatible with questions by itself, but occasionally occurs in wh-RQs left of DiPs like schon instead of denn. The results of two acceptability judgment experiments confirm that ja is strongly dispreferred in RQs, the presence of schon improves RQs with and without ja, and denn has no effect on acceptability. A follow-up study further indicated the rhetorical reading of our target questions to prevail independently from DiPs. We conclude that ja in RQs operates on the information contributed by elements like schon, denoting roughly that the issue in question arises ‘unquestionably against expectations’. Our contexts were neutral regarding the discourse functions of ja and denn (side remarks vs. QuDs), unlike the contexts of the findings, from which we deduce that the marked ja schon-RQs, while grammatical, require specific felicity conditions. A first attempt to confirm this experimentally was globally unsuccessful and could only reveal potential hints in an exploratory analysis.