Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Personalized Book Recommendation using Social Media Analysis

: Today, most e-commerce sites use product-specific recommendation systems to better user experience. The algorithm used by such sites is - item-to-item collaborative filtering. This matches each user who has purchased and rated items to similar items and then combines those similar items into a recommendation list. The solution proposed in this paper is an integrated book recommendation system that maps the user’s highly rated books with books of a similar genre, maps the interactions of said user on social media to assess the kind of books one is interested in, and considers the collaborative filtering or association mapping between the items. In this model, authors used datasets for the same Goodreads book collection, Amazon and Goodreads reviews, transaction histories, and Twitter data. The proposed solution shall use a weighted measure, k-means clustering, and sentiment analysis. The collaborative filtering will be done using the Apriori mechanism to develop an integrated book recommendation list. The result is a list of 10 books that are recommended for a particular user. The proposed model met 80 percent of the user’s expected recommendations, whereas the simple collaborative model only met 60 percent of the user’s expectations. The collaborative model consisted majority of books by the same author or of a complete contrast genre as the method only considers the choice of other similar users and not similar books. So, the proposed integrated recommendation system is more accurate in its recommendations than a simple collaborative system. This model helps firms recommend the best possible book for book lovers. It also helps book lovers to find the best content as per their interests.
Time Series Anomaly Detection using Skip-Step Contrastive Predictive Coding

Self-supervised learning (SSL) shows impressive performance in many tasks lacking sufficient labels. In this paper, we study SSL in time series anomaly detection (TSAD) by incorporating the characteristics of time series data. Specifically, we build an anomaly detection algorithm consisting of global pattern learning and local association learning. The global pattern learning module builds encoder and decoder to reconstruct the raw time series data to detect global anomalies. To complement the limitation of the global pattern learning that ignores local associations between anomaly points and their adjacent windows, we design a local association learning module, which leverages contrastive predictive coding (CPC) to transform the identification of anomaly points into positive pairs identification in contrastive learning. Motivated by the observation that adjusting the distance between the history window and the time point to be detected directly impacts the detection performance in the CPC framework, we further propose a skip-step CPC scheme in the local association learning module which adjusts the distance for better construction of the positive pairs and detection results. The experimental results show that the proposed algorithm achieves superior performance on multiple benchmark datasets in comparison with 11 state-of-the-art algorithms.
Parallel parking of a car-like mobile robot based on the P-domain path tracking controllers

This study presents high-precision path tracking controllers to follow a parking path in which change in the movement direction is required along it. Compared with other existing path tracking controllers, the automatic control of the movement direction and the necessity of zero velocity at the switching and end points of the parking manoeuvre are considered in the controller design. To this aim, two time-domain functions are proposed depending on the movement direction. Then, two function-domain tracking controllers based on feedback linearisation control and sliding mode control (SMC) structures are designed. The stability of the tracking controllers is analysed and guaranteed. Unlike the conventional SMC, the control signals of the function-domain SMC are very smooth. Moreover, designing the tracking controller in the function-domain can be a solution to modify the tracking controllers that have problem in the control law for the steering angle at zero speed. In the proposed controllers, the steering angle and the driving velocity are fully controlled by the controllers, and the robot automatically stops at the critical points independent of the position estimation and velocity tracking errors. Simulation results demonstrate the effectiveness of the proposed controllers to steer the robot along the reference path.
Generic Security of the Ascon Mode: On the Power of Key Blinding

The Ascon authenticated encryption scheme has recently been selected as winner of the NIST Lightweight Cryptography competition. Despite its fame, however, there is no known overall generic security treatment of its mode: most importantly, all earlier related generic security results only use the key to initialize the state and do not take into account key blinding internally and at the end. In this work we present a thorough security analysis of the Ascon mode: we consider multi-user and possibly nonce-misuse security by default, but more importantly, we particularly investigate the role of the key blinding. More technically, our analysis includes an authenticity study in various attack settings. This analysis includes a description of a security model of authenticity under state recovery, that captures the idea that the mode aims to still guarantee authenticity and security against key recovery even if an inner state is revealed to the adversary in some way, for instance through leakage. We prove that Ascon satisﬁes this security property, thanks to its unique key blinding technique.
Contrastive Learning for Multi-Label Classification

Contrastive learning is one of the most popular methods in self-supervised learning in recent years, achieving state-of-the-art performance in unsupervised training of deep neural networks for computer vision tasks. However, these methods mainly deal with binary classification, leaving out multi-label tasks, where additional dependency information between classes is needed to achieve satisfying performance. Although several proposed methods in contrastive learning have been developed to learn complex cross-label structures, they rely on specific architectures that introduce constraints and computational complexity. In this work, we propose a novel approach to contrastive supervised learning for multi-label classification (MultiSupCon). The main contribution is a new loss function that allows us to gain knowledge about the degree of label overlap between pairs of samples. We analyze our MultiSupCon loss to achieve best performance on CelebA dataset with top-1 mAP of 73.9% on the TResNet-M model, which is 2.8% above the best results for compared contrastive learning methods reported for this architecture.
Collaborative Inference of Combined Invariants

Inductive invariant inference is the fundamental problem in program verification, and specifically in verification of functional programs that use nonlinear recursion and algebraic data types (ADTs). For ADTs, it is challenging to come up with an abstract domain that is rich enough to represent program properties and a procedure for invariant inference which is effective for this domain. Although there are various techniques for different abstract domains for ADTs, they often diverge while analyzing real-life programs because of low expressivity of their abstract domains. Moreover, it is often unclear if they could comple- ment each other, other than by running in a portfolio. We present a lightweight approach to combining any existing techniques for different abstract domains collaboratively, thus targeting a more expressive domain. We instantiate the approach and obtain an effective inductive invariant inference algorithm in a rich combined domain of elementary and reg- ular ADT invariants essentially for free. Because of the richer domain, collaborations of verifiers are capable of solving problems that are beyond the capabilities of the collabora- tors running independently. Our implementation of the algorithm is a collaboration of two existing state-of-the-art inductive invariant inference engines having general-purpose first- order logic solvers as a backend. Finally, we show that our implementation is capable of solving a large amount of CHC-Comp 2022 problems obtained from Haskell verification problems, for which the existing tools diverge.
Finding Simplex Items in Data Streams

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate" the two machine learning models through end-to-end experiments. We have released our source code at GitHub.
Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms

—One of the main functions of NLP (Natural Language Processing) is to analyze a sentiment or opinion of the text considered. In this research the objective is to analyze the sentiment in the form of tweets towards the Covid-19 vaccination. In this study, the collected tweets are in the form of a dataset from Kaggle that have been categorized into positive and negative depending on the polarity of the sentiment in that tweet, to visualize the overall situation. The reviews are translated into vector representations using various techniques, including Bag-Of-Words and TF-IDF to ensure the best result. Machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine (SVM) and others, and Deep Learning algorithms like LSTM and Bert were used to train the predictive models. The performance metrics used to test the performance of the models show that Support Vector Machine (SVM) achieved the highest accuracy of 88.7989% among the machine learning models. Compared to the related research papers the highest accuracy obtained using LSTM is 90.59 % and our model has predicted with the highest accuracy of 90.42% using BERT techniques.
Machine-Checked Security for XMSS as in RFC 8391 and SPHINCS +

This work presents a novel machine-checked tight security proof for XMSS — a stateful hash-based signature scheme that is (1) standardized in RFC 8391 and NIST SP 800-208, and (2) employed as a primary building block of SPHINCS + , one of the signature schemes recently selected for standardization as a result of NIST’s post-quantum competition. In2020,Kudinov, Kiktenko, and Fedoro pointed out a flaw affecting the tight security proofs of SPHINCS + and XMSS. For the case of SPHINCS + , this flaw was fixed in a subsequent tight security proof by Hülsing and Kudinov. Unfortunately, employing the fix from this proof to construct an analogous tight security proof for XMSS would merely demonstrate security with respect to an insufficient notion. At the
A simple shelf-life estimation method as an integral part of a total dairy quality assurance program

As part of a total dairy quality control program along with standard Plate Count, Coliform count, and sensory evaluation, there needs to be a simple, rapid method for assessing dairy product quality and/or estimating shelf-life. Such a method exists which involves preliminary incubation of a milk sample at 21 o C for 18h, followed by plating in/on Standard Methods Media which is incubated at 21 o C for 25-48h. Bacterial counts generated can be used to «categorize» milk as to≤10d, 10-14d, or≥14d shelf-life as determined by sensory evaluation
VarietyQuest: Data-Driven Exploration of Apple Fruit Variety Classification Frameworks

: The Kashmir valley is known for its breathtaking landscapes and the cultivation of a variety of fruits, with apples being one of the most popular. The Kashmiri apple has a distinct flavor and is widely appreciated for its quality, leading to its export to various destinations around the globe. The sorting and packaging of apples remain a significant challenge due to the lack of skilled labor and the sheer volume of apples produced in the season. As a result, a considerable amount of the harvest is either lost or damaged, leading to significant financial losses. To address this issue, automated classification systems for categorizing fruit varieties using machine learning-based techniques are being developed. Such systems could potentially lead to increased productivity while simultaneously reducing labor costs and errors in sorting and classification. This article provides an overview of the data-driven methodologies for the automated categorization of apple varieties, exploring both conventional and state-of-the-art approaches. The article also provides a concise discussion of the datasets employed in these frameworks. Our study identifies machine learning as a critical foundation for most Apple variety classification frameworks. Further, the absence of datasets for the Kashmiri apple variety is noted during the survey, highlighting the need for further research in this domain. Overall this research explores the automated classification and packaging systems, which can streamline the process and minimize losses while contributing to the growth of the Apple industry across the globe.
KY3F10:Er3+/Yb3+ nanocrystals doped laser-induced self-written waveguide for optical amplification in the C-band

We successfully synthesized Er3+/Yb3+ co-doped KY3F10 nanocrystals by a facile hydrothermal method. The average size of the as-prepared nanocrystals was about 60 nm based on the observation of scanning electron microscope. Under the excitation of a 976 nm laser, the Er3+/Yb3+ doped KY3F10 nanocrystals showed intense near-infrared emission band centered at 1539 nm. The optimal concentrations of Er3+ were carefully selected according to the quantum yield measurement for a stronger emission in the C-band. The as-prepared nanocrystals were dispersed into a monomer, bisphenol A ethoxylate diacrylates, in which self-written waveguides can be fabricated under the irradiation of an induced laser at 450 nm. The KY3F10: Er3+/Yb3+ nanocrystals embedded polymer waveguide were fabricated by laser-induced self-written technique. Two pieces of single mode fiber were well connected with the waveguide in the fabrication procedure. Under a 976 nm laser pumping, amplified spontaneous emission at 1539 nm was observed in the KY3F10: Er3+/Yb3+ nanocrystals doped waveguide.
Factors Influencing Small Business Success in Medical Equipment Manufacturing Industry

Research Question, Aim and Objectives What aspects of the business environment encourage the growth of minor medical equipment manufacturers? How much do the difficulties small businesses face when attempting to manufacture medical equipment hinder their possibilities of achieving financial and operational success? How much do factors such as product quality, pricing, marketing, and supply chain management contribute to the success of small enterprises in the medical equipment manufacturing industry?
F ORMATION AND M ITIGATION OF T ECHNOSTRESS IN THE P ERSONAL U SE OF IT 1

Understanding information technology (IT) use is vital for the information systems (IS) discipline due to its substantial positive and negative consequences. In recent years, IT use for personal purposes has grown rapidly. Although personal use is voluntary and can often reflect fun, technostress is a common negative consequence of such use. When left unaddressed, technostress can cause serious harm to IT users. However, prior research has not explained how technostress forms over time or how its mitigation takes place in a personal—rather than organizational—environment. To address these research gaps, we conducted a qualitative study with narrative interviews of IT users who had experienced technostress. This study contributes to (1) the technostress literature by unpacking states in which technostress forms and can be mitigated and (2) the IT affordance literature by explaining the role of affordances and their actualizations in technostress as well as introducing the new concept of actualization cost. In terms of practice, our findings help individuals and societies identify the development of technostress, understand the activities required for its mitigation, and recognize mitigation barriers.
Epidemiology of chronic pain and opioid use in primary care - a scoping review of big data research

Objective Recent research addressing the opioid use and misuse crisis in patients with chronic non-cancer pain in primary care has focused on traditional cohort studies underpinned by survey data. The advent of electronic health records creates a 'big data' opportunity for improving our understanding of the epidemiology of chronic non-cancer pain in primary care and opioid use and misuse. This scoping review aimed to map the chronic non-cancer pain patient population in primary care using big data research, investigating the patient characteristics and opioid prescription patterns. Methods Searches of primary electronic databases and grey literature, including OVID, CINAHL, and Scopus, were performed from January 1, 2010 to December 2, 2022. The search strategy was restricted to the English language. Results A total of 1,057 records from databases and 515 records from grey literature were considered. Of these, only three articles met the eligibility criteria, and two articles of these reported an estimated chronic pain prevalence of 3.82% and 10.3% in the primary care setting. Chronic pain patients that presented to primary care providers were predominately female, and common comorbidities were anxiety and depression. An estimated 30% of chronic pain patients used opioids for treatment sourced from general practitioners and family practitioners. Conclusion The use of big data remains underutilized for investigating the epidemiology of chronic pain and opioid use in primary care. This review calls for a greater focus on pain informatics with big data to improve the accuracy of future clinical chronic pain epidemiology studies.
The Quality of KUB Rooster Sperm with Vitamin E and C Supplementation During Room Temperature Storage

Antioxidant supplementation to semen extenders can help to maintain rooster sperm quality. This study determined how vitamins E and C affected the quality of KUB sperm at room temperature. Semen collection from 12 KUB roosters used dorso abdominal massage method, twice a week. Semen samples were then diluted using LR-EY extender with three treatments: LR-EY without antioxidant as the control, LR-EY supplemented 0.05 mg/ml vitamin E, and 0.2 mg/ml vitamin C. The diluted semen samples were then stored at room temperature, then motility and viability were examined at 0, 1st, 2nd, and 4th hour. The results showed that the percentage of motility between treatment groups and time points differed not significantly (P>0.05). However, vitamin E supplementation had a significant (P<0,05) effect on the percentage of viability in the 2nd and 4th hour. Supplementation of vitamin E 0.05 mg/ml was able to maintain the viability of KUB rooster sperm at 2nd hour (88.75 ± 1.50%) and 4th hour (84.75 ± 2.63%). It can be concluded that vitamin C cannot maintain sperm quality significantly, but vitamin E can preserve the viability of KUB rooster sperm significantly at room temperature.
Comparison of the combinations of water and ethanol extract of Strychnos ligustrina blum wood with Dihydroartemisinin- Piperaquin Phosphate (DHP) as an anti-malaria in mice infected P. berghei ANKA

One of the most significant global public health issues is malaria. Combination treatment of S. ligustrina blum wood with Dihydroartemisinin-piperaquin phosphate (DHP) as an antimalaria to increase the effectiveness of S. ligustrina blum wood extract. The purpose of this study was to see how effective the combination of aquadest extract (AE) and 25% ethanol extract (EE) of S. ligustrina blum wood with DHP was against P. berghei ANKA. S. ligustrina blum wood extract were extracted by maceration using aquadest and 25% ethanol. The extract formulated into capsules with or without DHP. Fifty DDY mice were divided into 10 groups (n = 5). Group I was infected infected-untreated, group II was received 25 mg/kg BW of dihydroartemisinin and 197 mg/kg BW of piperaquine phospate, and the others group of mice inoculated with 1x106 P. berghei ANKA and treated with 200 mg/kg. Blood samples were taken every day, starting from the first day to the tenth day. The number of parasites and erythrocytes was calculated using the image photoshop program, then the percentage of parasitemia and inhibition was determined. There was no difference between AE and EE in combination with half dose of DHP with DHP control, but significantly different with infection control. S. ligustrina blum wood extract worked synergistic with DHP as an antimalaria with a percentage of inhibition approaching 100% on the 10th day after treatment. S. ligustrina blum wood extract has the potential to be developed as an effective and safe combination antimalaria drug with pure chemical active ingredients.
Solitary propagation effect of a well-defined chirped femtosecond laser pulse in a resonance-absorbing medium

We investigate the solitary propagation effect of a well-defined chirped femtosecond laser pulse in a resonance-absorbing medium which is modeled by a two-level quantum dot ensemble. Employing full time-dependent and space-dependent Maxwell-Bloch equations, the evolution of the pulse and the population inversion as well as the spectrum are obtained. The results reveal that the special chirped pulse can retain its shape and resist breakup while propagating through the absorbing medium. Besides, the spectrum does not exhibit obvious broadening but the central and near-central frequency components tend to vanish. Moreover, the stable soliton formed by the special chirped-pulse design based on stimulated Raman adiabatic passage does not obey the area theorem.
Two-weight norm estimates for maximal and Calderón–Zygmund operators in variable exponent Lebesgue spaces

Abstract. Two-weight norm estimates for Hardy-type transforms, maximal functions and singular integrals in variable exponent Lebesgue spaces defined, generally speaking, on spaces of homogeneous type (quasi-metric measure spaces with doubling measure) are established. The derived conditions are written in terms of norms and are simultaneously necessary and sufficient for appropriate inequalities mainly in the case when weights are of radial type. An appropriate example of a pair of non-Muckenhoupt weights governing the two-weight estimate for maximal and Calderón–Zygmund operators is also constructed.