Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application

In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characterized. Instead, new unknown threats, often referred to as zero-day attacks or zero-days, likely go undetected as they are often misclassified by those techniques. In recent years, unsupervised anomaly detection algorithms showed potential to detect zero-days. However, dedicated support for quantitative analyses of unsupervised anomaly detection algorithms is still scarce and often does not promote meta-learning, which has potential to improve classification performance. To such extent, this paper introduces the problem of zero-days and reviews unsupervised algorithms for their detection. Then, the paper applies a question-answer approach to identify typical issues in conducting quantitative analyses for zero-days detection, and shows how to setup and exercise unsupervised algorithms with appropriate tooling. Using a very recent attack dataset, we debate on i) the impact of features on the detection performance of unsupervised algorithms, ii) the relevant metrics to evaluate intrusion detectors, iii) means to compare multiple unsupervised algorithms, iv) the application of meta-learning to reduce misclassifications. Ultimately, v) we measure detection performance of unsupervised anomaly detection algorithms with respect to zero-days. Overall, the paper exemplifies how to practically orchestrate and apply an appropriate methodology, process and tool, providing even non-experts with means to select appropriate strategies to deal with zero-days.
Photovoltaic Performance Improvement of D–A Copolymers Containing Bithiazole Acceptor Unit by Using Bithiophene Bridges

Two bithiophene-bridged D–A copolymers, PDTSBTBTz and PBDTBTBTz, based on bithiazole acceptor unit and dithienosiole (DTS) or benzodithiophene (BDT) donor unit, were synthesized by the Pd-catalyzed Stille-coupling reaction. The two copolymers exhibit good thermal stability, strong absorption in the visible region, and relatively lower HOMO energy level at ca. −5.10 eV. The hole mobilities of PDTSBTBTz and PBDTBTBTz measured by SCLC method are 1.85 × 10–3 and 1.77 × 10–3 cm2/(V s), respectively. Power conversion efficiency (PCE) of the polymer solar cell (PSC) based on PDTSBTBTz: PC70BM (1:1, w/w) was 3.82% with Jsc = 8.68 mA/cm2, Voc = 0.72 V, and FF = 0.611, under the illumination of AM1.5, 100 mW/cm2. In contrast, the PCE of the PSC based on PBDTBTBTz:PC70BM (1:1, w/w) reached 4.46% with Jsc = 9.01 mA/cm2, Voc = 0.82 V, and FF = 0.603. These results indicate that bithiophene-bridged D–A copolymers are promising photovoltaic donor materials for the application in PSCs.
Deep Speaker Recognition: Process, Progress, and Challenges

Speaker recognition is related to human biometrics dealing with the identification of speakers from their speech. Speaker recognition is an active research area and being widely investigated using artificially intelligent mechanisms. Though speaker recognition systems were previously constructed using handcrafted statistical means of machine learning, currently it is being shifted to state-of-the-art deep learning strategies. Further, deep learning being a fast-paced domain, an absence of comprehensive survey is observed in the current deep speaker recognition technologies. In this paper, we focus on deep speaker recognition technologies. The paper particularly introduces a taxonomy, explains the progress, architectural strategies and processes of some distinctive approaches. Further, the manuscript classifies and enlists the currently available datasets and programming tools. Finally, the paper investigates the challenges and future directives of deep speaker recognition technology.
Identifying Metrics for an IoT Performance Estimation Framework

In this work we introduce a framework to support design decisions for heterogeneous IoT platforms and devices. The framework methodology as well as the development of software and hardware models are outlined. Specific factors that affect the performance of device are identified and formulated in a metric form. The performance aspects are embedded in a flexible and scalable framework for decision support. An indicative experimental setup investigates the applicability of the framework for a specific functional block. The experimental results are used to assess the significance of the framework under development.
The Effect of Spatial Design on User Memory Performance Using the Method of Loci in VR

Based on the Method of Loci, the following experiment compares the effect of two different virtual environments on participants' memory performance. The primary task consists of remembering a sequence of random playing cards. Each virtual environment is based on a different architectural style with a different layout. One is inspired by a Palladian style architecture, and the other by a Modern curved architecture.
A Novel Single-Element Inductance-to-Digital Converter with Automatic Offset Eliminator

A novel Inductance-to-Digital Converter (LDC) that outputs a digital value corresponding to the change in inductance ($\Delta L$) of a single element inductive sensor is presented. In most cases, the offset inductance $L_{0}$ offered by the sensor will be large as compared to $\Delta L$ (due to the measurand). Thus, the existing architectures that measure the sensor inductance $L_{X}(= L_{0}\pm\Delta L)$ lead to underutilization of the output range and hence such systems cannot focus on sensing the measurand alone. The proposed LDC automatically eliminates the offset inductance in the final output and utilizes the entire digital scale only for representing $\Delta L$. Also, the digital output of the proposed LDC is made independent of the coil resistance $R_{C}$ of the inductive sensor. The proposed LDC employs DC source for digitization, thereby eliminates the need for AC excitation sources that demands repeatable sinusoidal amplitude for high accuracy measurements. The direct digital conversion is performed by modifying the dual-slope conversion technique. A prototype of the LDC was developed and verified the automatic offset elimination operation of the proposed converter. The prototype exhibited a worst-case linearity error of 0.5% for $\Delta L$ ranging from −9 mH to +9 mH.
Text Analysis Methods for Misinformation-Related Research on Finnish Language Twitter

The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stems together with derivation and compounding. Furthermore, there is a need to understand which themes linked with misinformation—and the concepts related to it—manifest in different countries and language areas in Twitter discourse. To address this issue, this study explores misinformation and its related concepts: disinformation, fake news, and propaganda in Finnish language tweets. We utilized (1) word cloud clustering, (2) topic modeling, and (3) word count analysis and clustering to detect and analyze misinformation-related concepts and themes connected to those concepts in Finnish language Twitter discussions. Our results are two-fold: (1) those concerning the functional data analysis methods and (2) those about the themes connected in discourse to the misinformation-related concepts. We noticed that each utilized method individually has critical limitations, especially all the automated analysis methods processing for the Finnish language, yet when combined they bring value to the analysis. Moreover, we discovered that politics, both internal and external, are prominent in the Twitter discussions in connection with misinformation and its related concepts of disinformation, fake news, and propaganda.
Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection

The integration of artificial intelligence (AI) and the Internet of Things (IoT) has tremendous prospects in smart healthcare. The advancement of AI in the form of deep learning brought a revolution in automatic classification and detection systems. In addition, next-generation wireless communications such as 5G networking brought speed and the seamless transmission of data. With the convergence of these elements, the smart healthcare sector is currently booming. Particularly during the post-COVID-19 pandemic, the necessity of smart healthcare has come to light more than before. A significant number of people suffer from voice pathology. This pathology can be easily cured if detected early. In this study, a voice pathology detection system within a smart healthcare framework is proposed. The inputs are obtained by the IoT, namely microphones and electroglottography (EGG) devices to capture voice and EGG signals, respectively. Spectrograms are obtained from these signals and fed into a pretrained convolutional neural network (CNN). The features extracted from the CNN are fused and processed using a bi-directional long short-term memory network. The proposed system is evaluated using a publicly available database, called the Saarbruecken voice database. The experimental results show that bimodal input performs better than a single input. An accuracy of 95.65% is obtained for the proposed system.
Generation of Assessment Questions from Textbooks Enriched with Knowledge Models

Augmenting digital textbooks with assessment material improves their eﬀectiveness as learning tools. It can be a laborious task requiring considerable amount of time and expertise. This paper presents an automated assessment generation tool that works as a component of the Intextbooks platform. Intextbooks extracts ﬁne-grained knowledge models from PDF textbooks and converts them into semantically annotated learning resources. With the help of the developed assessment components, these textbooks become interactive educational tools capable to assess students’ knowledge of relevant concepts. The results of an expert-based pilot evaluation show that generated questions are properly worded and have a good range in term of diﬃculty. From the point of assessment value, some generated questions types fall behind manually constructed assessment, while others obtain comparable results.
Digitally enabled interactions: Designing for Customer Agency, Control and Customization

Designing service processes to receive digital inputs from customers is a key challenge for service providers. This design has an impact on the outcomes of the service process for the provider and customer. These impacts can be customization, control, and customer agency. Service process activities receive customer information digitally through surrogate interactions using customer and provider digital technologies. These digital interactions are becoming the dominant method for obtaining customer resources and information to provide service. A design of learning processes to achieve customization and control is illustrated to show how these outcomes provide customer agency and satisfaction. Traditional service design challenges and trade-offs are mitigated through designing services for digital interactions.
Reconstruction of Galvanic Skin Response Peaks via Sparse Representation

Continuous and long-term measurement of physiological signals out of clinical settings may face different processing requirements resulting in higher costs or reduced performance. Improved techniques of signal reconstruction from compressed representation may be a solution. This paper presents an approach based on Compressed Sensing to reconstruct peaks of Galvanic Skin Response measured by a wrist-worn device. Specifically, a random measurement matrix is employed in the reconstruction phase. Results show that the proposed approach detects the correct number of peaks better than the Ledalab automatic toolbox, even with high compression rates.
Clustering versus Statistical Analysis for SCA: when Machine Learning is Better

Evaluation of the resistance of implemented cryptographic algorithms against SCA attacks, as well as detecting of SCA leakage sources at an early stage of the design process, is important for an efficient re-design of the implementation. Thus, effective SCA methods that do not depend on the key processed in the cryptographic operations are beneficially and can be a part of the efficient design methodology for implementing cryptographic approaches. In this work we compare two different methods that are used to analyse power traces of elliptic curve point multiplications. The first method the comparison to the mean is a simple method based on statistical analysis. The second one is K-means - the mostly used unsupervised machine learning algorithm for data clustering. The results of our early work showed that the machine learning algorithm was not superior to the simple approach. In this work we concentrate on the comparison of the attack results using both analysis methods with the goal to understand their benefits and drawbacks. Our results show that the comparison to the mean works properly only if the scalar processed during the attacked kP execution is balanced, i.e. if the number of ‘1’ in the scalar $k$ is about as high as the number of ‘0’. In contrast to this, K-means is effective also if the scalar is highly unbalanced. It is still effective even if the scalar $k$ contains only a very small number of ‘0’ bits.
A Method of Entropy Weight Quantitative Risk Assessment for the Safety and Security Integration of a Typical Industrial Control System

Aiming at the risk assessment requirements of typical industrial control systems with integrated architecture of security and safety, we propose an objective and quantitative integrated security and safety assessment scheme based on Fuzzy Analytic Hierarchy Process (FAHP). First, we establish a safety and security integrated (SSI) architecture for typical industrial control systems with security measures integrated into safety failure modes. On this basis, we establish a hierarchical model of risk assessment with SSI failure mode as an element of the evaluation layer, and then standardize characteristic values of various safety-related heterogeneous index parameters. We design an entropy weight method that uses Grey Relation Analysis (GRA) method to modify the correlation of multiple indicators as a parameter strategy for determining the relative importance of element layer and evaluation layer and then use the membership function method of fuzzy statistical method to obtain the membership degree of hierarchical elements, and finally obtain the failure risk level value of equipment and system by fuzzy comprehensive evaluation. Based on a typical distributed control system, we build an experimental platform to test and verify the risk assessment plan, and compared with expert experience parameter method. The result shows that the scheme takes into account the correlation between indicators which measure the SSI risk level of industrial control system, and the entropy weight method is used to evaluate the risk of industrial control system which can overcome the subjectivity and uncertainty of individual judgment. Furthermore, the quantitative evaluation of system risk is completed by using fuzzy statistical method in the case of industrial control system without prior knowledge, and the idea of this scheme has a wide range of engineering value.
Tunable Plasmon Resonances and Enhanced Local Fields of Spherical Nanocrescents

The tunable plasmon resonances of nanostructures with sharp features play an important role in the surface-enhanced spectroscopy. The maximum enhancement factor can be achieved by tuning the resonance wavelengths. In this study, the discrete-dipole approximation was used to investigate the resonance modes of the spherical nanocrescents that possess the hot spots located at the ring-tip. We applied the plasmon hybridization theory to interpret the excitation and shift of resonance modes of the nanostructure with reduced symmetry. The resonance wavelengths can be tuned from the visible to the near-infrared regime by varying the geometrical parameters. The intraparticle coupling at the ring-tip explained the significant influence of the incident light polarization on the enhancement of local fields. The spherical nanocrescents can be developed as a powerful substrate in surface-enhanced spectroscopy for the tunable plasmon resonances and enhanced local fields.
Synthesis and Dynamic Behavior of Chiral NNO‐Scorpionate Zinc Initiators for the Ring‐Opening Polymerization of Cyclic Esters

The reaction of chiral alcohol–scorpionate compounds bpzbeH [bpzbeH = 1,1-bis(3,5-dimethylpyrazol-1-yl)-3,3-dimethyl-2-butanol] or bpzteH [bpzteH = 2,2-bis(3,5-dimethylpyrazol-1-yl)-1-para-tolylethanol] with [ZnR2] (R = Me, Et, CH2SiMe3) in a 1:2 molar ratio afforded the dinuclear chiral zinc alkyls [Zn(R)(κ2-NNµ-O)Zn(R)2] (1–4) [κ2-NNµ-O = bpzbe, R = Me (1), Et (2), CH2SiMe3 (3); bpzte, R = Et (4)]. Subsequent alcoholysis or thioalcoholysis reaction with ArEH (1 equiv.; E = O, S; Ar = 2,6-C6H3Me2) yielded the chiral dinuclear mixed alkyl–alryl oxides/thioaryl oxides [(ZnR)2(κN:κN-µ-O)(µ-EAr)] (5–12) [κN:κN-µ-O = bpzbe, E = O, R = Me (5), Et (6), CH2SiMe3 (7); bpzte, E = O, R = Et (8); bpzbe, E = S, R = Me (9), Et (10), CH2SiMe3 (11); bpzte, E = S, R = Et (12)]. The alcoholysis reaction of the previously reported monoalkyls [Zn(Me)(κ3-NNO)] or [Zn(R)(κ-NNµ-O)]2 with ArOH (1 equiv., Ar = 2,6-C6H3Me2) afforded the chiral aryl oxides [Zn(OAr)(κ2-NNµ-O)]2 (13–14) [κ2-NNµ-O = bpzbe (13); bpzte (14)]. The X-ray crystal structures of 3, 5, 6, and 14 confirmed a dinuclear structure in all cases with the alkoxide of the heteroscorpionates in a µ-bridging mode between the ZnII centers. Variable-temperature NMR spectroscopic studies were carried out to study their dynamic behavior in solution. Complexes 1–9 and 12 can act as single-component initiators for the ROP of e-CL and l-/rac-LA, and afforded materials with low molecular weights and narrow monomodal molecular weight distributions. MALDI-TOF mass spectra confirmed that for 5–8 the initiation occurred through nucleophilic attack by the alkoxide group, rather than the alkyl group, on the lactide monomer. Furthermore, inspection of the kinetic parameters showed propagations with a pseudo-first-order dependence on monomer and catalyst concentrations. Microstructural analysis of poly(rac-lactide) revealed that the mixed alkyl/aryl oxide-substituted initiators exert a moderate level of heteroselectivity (Ps = 0.66).
The relationship between anthropogenic dust and population over global semi-arid regions

Abstract. Although anthropogenic dust has received more attention from the climate research community, its dominant role in the production process is still not identified. In this study, we analysed the relationship between anthropogenic dust and population density/change over global semi-arid regions and found that semi-arid regions are major source regions in producing anthropogenic dust. The results showed that the relationship between anthropogenic dust and population is more obvious in cropland than in other land cover types (crop mosaics, grassland, and urbanized regions) and that the production of anthropogenic dust increases as the population density grows to more than 90 persons km−2. Four selected semi-arid regions, namely East China, India, North America, and North Africa, were used to explore the relationship between anthropogenic dust production and regional population. The most significant relationship between anthropogenic dust and population occurred in an Indian semi-arid region that had a greater portion of cropland, and the high peak of anthropogenic dust probability appeared with 220 persons km−2 of population density and 60 persons km−2 of population change. These results suggest that the influence of population on production of anthropogenic dust in semi-arid regions is obvious in cropland regions. However, the impact does not always have a positive contribution to the production of anthropogenic dust, and overly excessive population will suppress the increase of anthropogenic dust. Moreover, radiative and climate effects of increasing anthropogenic dust need more investigation.
The analysis of raw spirits – a review of methodology

Agricultural distillates (raw spirits) are alcoholic liquids obtained through distillation (preceded by alcoholic fermentation) of specific agricultural products that do not have the properties of ethyl alcohol or a spirit, but still retain the aroma and taste of the raw ingredients used. This review is a brief overview of agricultural distillates and of some methods commonly used (GC-MS, GC-FID, GC-O, electronic nose) for performing quality assessments and for determining the composition and botanical origins of the raw spirits. Copyright © 2016 The Institute of Brewing & Distilling
Spastic Paraplegia-56 due to a Novel CYP2U1 Truncating Mutation in an Indian Boy: A New Report and Literature Review

Spastic paraplegia-56 is a rare autosomal recessive disorder, caused by homozygous or compound heterozygous mutations in the CYP2U1 gene, located on chromosome 4. Till date, only 28 patients with this disorder have been reported in the literature. We report a new case of CYP2U1-related spastic paraplegia-56. We also reviewed previously published patients with this condition from various databases. Next-generation sequencing in the index child detected a novel homozygous two base pair deletion in exon 2 of the CYP2U1 gene that results in a frameshift and premature truncation of the protein 19 amino-acid downstream to codon 361. Together with the presented case, 29 were available for analysis. The mean age at the diagnosis was 17.84 ± 6.86 years. Intellectual disability/cognitive dysfunction and delayed walking or gait disturbance were the most common presenting features. Around half of the patients had neuroregression in between 1 and 2 years. It is clinically imperative to suspect this disease in children with early-onset spastic paraparesis, especially in cases accompanied by baseline development delay or cognitive impairment and consanguinity.
Original article Evaluation of the stability of Bacillus coagulans MTCC 5856 during processing and storage of functional foods

Summary The study was aimed to find out the stability of Bacillus coagulans MTCC 5856 in functional foods during processing and storage. B. coagulans MTCC 5856 was found to be stable during baking and storage at frozen conditions of banana muffins (92% viability) and waffles (86% viability) upto 12 months. After brewing coffee at 90 °C for 2 min, there was 87% viability and retained 66% viability even after maintaining temperature at 77 °C for 4.0 h. It showed over 95% viability in chocolate fudge frosting, hot fudge toppings, peanut butter, strawberry preserve and vegetable oil at room temperature upto 12 months. It was found to be stable in apple juice upto 6 months at refrigerated condition and concentrated glucose syrup at 4 � 3 and 25 � 2 °C upto 24 months. B. coagulans MTCC 5856 showed promising stability during processing and storage of functional foods and could be an excellent probiotic ingredient to use in various food applications.