Miracle-dz/UniFAR-FTUs huggingface dataset:
An Obfuscated Attack Detection Approach for Collaborative Recommender Systems

In the recent times, we have loads and loads of information available over the Internet. It has become very cumbersome to extract relevant information out of this huge amount of available information. So to avoid this problem, recommender systems came into play, which can predict outcomes according to user's interests. Although recommender systems are very effective and useful for users, the most used type of recommender system, i.e. collaborative filtering recommender system, suffers from shilling/profile injection attacks in which fake profiles are inserted into the database in order to bias its output. With this problem in mind,we propose an approach to detect attacks on recommender systems using Random Forest Classifier and find that, when tested at 10% attack, our approach outperformed earlier proposed approaches.
Language Informed Modeling of Code-Switched Text

Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities. CS texts have a complex interplay between languages and occur in informal contexts that make them harder to collect and construct NLP tools for. We approach this problem through Language Modeling (LM) on a new Hindi-English mixed corpus containing 59,189 unique sentences collected from blogging websites. We implement and discuss different Language Models derived from a multi-layered LSTM architecture. We hypothesize that encoding language information strengthens a language model by helping to learn code-switching points. We show that our highest performing model achieves a test perplexity of 19.52 on the CS corpus that we collected and processed. On this data we demonstrate that our performance is an improvement over AWD-LSTM LM (a recent state of the art on monolingual English).
Selenium maintains Ca2+ homeostasis in sheep lymphocytes challenged by oxidative stress

Selenium (Se) is an essential element in human and animal diets, based upon a widespread range of beneficial effects that are primarily due to its antioxidant properties. While Se can be associated to anti-cancer and anti-diabetic activities, reproductive efficiency, and enhancement of the immune system, the mechanistic details of the corresponding biological processes are still largely elusive. To avoid deficiencies and increase bioavailability, Se it is generally supplied to livestock through Se-supplemented feeds or forage plants fertilized with inorganic Se. While the relationship between Ca2+ and ROS (reactive oxygen species) is well known, only a few studies have addressed the possible involvement of Se in the control of cytosolic Ca2+ in oxidative stress. The results on Ca2+ homeostasis were obtained adding exogenous Se in the form of SeO42- to sheep lymphomonocytes cultured in vitro. In particular, Se strongly attenuated 1mM H2O2-induced alteration of intracellular [Ca2+]C as well as the entry of extracellular Ca2+ into the cells with comparable EC50 values for sodium selenate accounting to 1.72 and 2.28 mM, respectively. In an ex vivo trial, it was observed that Ca2+ homeostasis can effectively be rescued in sheep lymphomonocytes exposed in vivo to a Se concentration of approximately 1.9 mM, that was achieved by feeding sheep with olive leaves previously sprayed with 500 mg/plant Na-selenate. Thus the results obtained suggest that the mode of action of selenium markedly influenced Ca2+-related signaling events. Furthermore, results clearly reveal that the protective effect of Se on Ca2+ homeostasis under oxidative challenge can be clearly and effectively achieved through an appropriate dietary regimen obtained also in a circular economy logic using pruning of olive trees treated to reduce tree drought stress.
Don't share, Don't lock: Large-scale Software Connection Tracking with Krononat

To simplify software updates and provide new services, ISPs are interested in migrating network functions implemented in residential gateways (such as DSL or Cable modems) to the cloud. Two key functions of residential gateways are Network Address Translation (NAT) and stateful firewalling, which both rely on connection tracking. To date, these functions cannot be efficiently implemented in the cloud: current OSes connection tracking is unable to meet the scale and reliability needs of ISPs, while hardware appliances are often too expensive. In this paper, we present Krononat, a distributed software NAT that runs on a cluster of commodity servers, providing a cost-efficient solution with an excellent reliability. To achieve this, Krononat relies on 3 key ideas: (i) sharding the connection tracking state across multiple servers, down to the core level; (ii) steering traffic exploiting the features of entry-level switches; and (iii) avoiding all locks and data sharing on the data path. Krononat supports a rate of 77 million packets per second on only 12 cores, tracking up to 60M connections. It is immune to single node failures, and supports elastic workloads by a fast reconfiguration mechanism (< 500ms).
Apprenticeship Bootstrapping: Inverse Reinforcement Learning in a Multi-Skill UAV-UGV Coordination Task

Apprenticeship learning enables learning from human demonstrations performed on tasks. However, acquiring demonstrations in complex tasks where a human expert is not available can be a challenge. In this paper, we propose a new learning algorithm, called Apprenticeship bootstrapping via Inverse Reinforcement Learning using Deep Q-learning (ABS via IRL-DQN), to learn a complex task through using demonstrations performed on primitive sub-tasks. The algorithm is evaluated on an aerial and ground coordination scenario, where an Unmanned Aerial Vehicle (UAV) is required to maintain three Unmanned Ground Vehicles (UGVs) within a field of view of the UAV 's camera (FoV). The results show that performance of our proposed algorithm is comparable to that of a human, and competitive to the original IRL using expert demonstrations performed on the composite task.
Using an Augmented Reality Cube-Like Interface and 3D Gesture-Based Interaction to Navigate and Manipulate Data

In this paper we describe our work-in-progress to create an interface that enables users to browse and select data within an Augmented Reality environment, using a virtual cube object that can be interacted with through 3D gestural input. We present the prototype design (including the graphical elements), describe the interaction possibilities of touching the cube with the hand/finger, and put the prototype into the context of our Augmented Reality for Public Engagement (PEAR) framework. An interactive prototype was implemented and runs on a typical off-the-shelf smart-phone device.
De Novo Transcriptome Assembly and Population Genetic Analyses for an Endangered Chinese Endemic Acer miaotaiense (Aceraceae)

Acer miaotaiense (P. C. Tsoong) is a rare and highly endangered plant in China. Because of the lack of genomic information and the limited number of available molecular markers, there are insufficient tools to determine the genetic diversity of this species. Here, 93,305 unigenes were obtained by multiple assembled contigs with a transcriptome sequencing program. Furthermore, 12,819 expressed sequence tag-derived simple sequence repeat (EST-SSR) markers were generated, 300 were randomly selected and synthesized, 19 primer pairs were identified as highly polymorphic (average number of alleles (Na) = 8, expected heterozygosity (He) = 0.635, polymorphism information content (PIC) = 0.604) and were further used for population genetic analysis. All 261 samples were grouped into two genetic clusters by UPGMA, a principal component analyses and a STRUCTURE analyses. A moderate level of genetic differentiation (genetic differentiation index (Fst) = 0.059–0.116, gene flow = 1.904–3.993) among the populations and the major genetic variance (81.01%) within populations were revealed by the AMOVA. Based on the results, scientific conservation strategies should be established using in situ and ex situ conservation strategies. The study provides useful genetic information for the protection of precious wild resources and for further research on the origin and evolution of this endangered plant and its related species.
A systematic review of the use of ketogenic diets in adult patients with cancer

BACKGROUND A growing body of evidence indicates the importance of nutrition in cancer treatment. Ketogenic diets are one strategy that has been proposed to enhance traditional anticancer therapy. This review summarises the evidence concerning the effect of oral ketogenic diets on anthropometry, metabolism, quality of life (QoL) and tumour effects, at the same time as documenting adverse events and adherence in patients with cancer. METHODS We searched electronic databases using medical subject headings (MeSH) and text words related to ketogenic diets and cancer. Adult patients following a ketogenic diet as a complementary therapy prior, alongside or after standard anticancer treatment for more than 7 days were included. Studies were assessed for quality using the Critical Appraisal Skills Programme tools (https://www.casp-uk.net). RESULTS Eleven studies were included with 102 participants (age range 34-87 years) from early-phase trials, cohort studies and case reports. Studies included participants with brain, rectal or mixed cancer sites at an early or advanced disease stage. The duration of intervention ranged from 2.4 to 134.7 weeks (0.5-31 months). Evidence was inconclusive for nutritional status and adverse events. Mixed results were observed for blood parameters, tumour effects and QoL. Adherence to diet was low (50 out of 102; 49%) and ranged from 23.5% to 100%. CONCLUSIONS High-quality evidence on the effect of ketogenic diets on anthropometry, metabolism, QoL and tumour effects is currently lacking in oncology patients. Heterogeneity between studies and low adherence to diet affects the current evidence. There is an obvious gap in the evidence, highlighting the need for controlled trials to fully evaluate the intervention.
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable

Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here. We make two contributions. First, we test a delightfully simple method for domain adaptation of bilingual word embeddings. We evaluate these embeddings on two bilingual tasks involving different domains: cross-lingual twitter sentiment classification and medical bilingual lexicon induction. Second, we tailor a broadly applicable semi-supervised classification method from computer vision to these tasks. We show that this method also helps in low-resource setups. Using both methods together we achieve large improvements over our baselines, by using only additional unlabeled data.
Social and Emotional Correlates of Capitalization on Twitter

Social media text is replete with unusual capitalization patterns. We posit that capitalizing a token like THIS performs two expressive functions: it marks a person socially, and marks certain parts of an utterance as more salient than others. Focusing on gender and sentiment, we illustrate using a corpus of tweets that capitalization appears in more negative than positive contexts, and is used more by females compared to males. Yet we find that both genders use capitalization in a similar way when expressing sentiment.
Robotic Process Automation

Enterprise procedures are a part of the everyday habitual of each business; they had been asked to play in a completely easy way, with no one to lose. historically, over the years humans try to automate procedures the usage of some of strategies with a purpose to encompass machines or device robots. many of the techniques which have been evolved formerly within the cutting-edge business or e-commercial enterprise are computerized and consequently there may be a want to avoid losses resulting from negative performance or sluggish system. the automated manner of using software program with a purpose to act as humans and perform duties on computer systems is stated because of the robotics’ automation technique. In fashionable, it isn't always uncommon to carry out techniques for terribly low to medium and high frequency. A corporation that automates its strategies using robotic procedure automation targets to acquire many benefits in phrases of reduced charges, advanced technique efficiency and a extensively reduced range of recycling sports inside the system.
Safety from Ethical Hazards: Prospects for a Contribution from Software Engineering

In this paper, I argue that while normative ethical concerns such as fairness and accountability must be addressed in the design of intelligent software, these concerns are far removed from traditional software engineering practice. After reviewing representative illustrations of such ethical hazards arising from the use of intelligent applications, I discuss current practices that might contribute to software engineering practices to assure that intelligent software is safe to use, i.e., free of ethical hazard.
DyCache: Dynamic Multi-Grain Cache Management for Irregular Memory Accesses on GPU

GPU utilizes the wide cache-line (128B) on-chip cache to provide high bandwidth and efficient memory accesses for applications with regularly-organized data structures. However, emerging applications exhibit a lot of irregular control flows and memory access patterns. Irregular memory accesses generate many fine-grain memory accesses to L1 data cache. This mismatching between fine-grain data accesses and the coarse-grain cache design makes the on-chip memory space more constrained and as a result, the frequency of cache line replacement increases and L1 data cache is utilized inefficiently. Fine-grain cache management is proposed to provide efficient cache management to improve the efficiency of data array utilization. Unlike other static fine-grain cache managements, we propose a dynamic multi-grain cache management, called DyCache, to resolve the inefficient use of L1 data cache. Through monitoring the memory access pattern of applications, DyCache can dynamically alter the cache management granularity in order to improve the performance of GPU for applications with irregular memory accesses while not impact the performance for regular applications. Our experiment demonstrates that DyCache can achieve a 40% geometric mean improvement on IPC for applications with irregular memory accesses against the baseline cache (128B), while for applications with regular memory accesses, DyCache does not degrade the performance.
Utilizing Social Media to Combat Opioid Addiction Epidemic: Automatic Detection of Opioid Users from Twitter

Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, in this paper, we propose a novel framework named AutoOPU to automatically detect the opioid users from Twitter, which will assist in sharpening our understanding toward the behavioral process of opioid addiction and treatment. In AutoOPU , to model the users and posted tweets as well as their rich relationships, we ﬁrst introduce a heterogeneous information network (HIN) for representation. Then we use meta-structure based approach to characterize the semantic relatedness over users. After-wards, we integrate content-based similarity and relatedness depicted by each meta-structure to formulate a similarity measure over users. Further, we aggregate different similarities using multi-kernel learning, each of which is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the ﬁrst work to use multi-kernel learning based on meta-structures over HIN for biomedical knowledge mining, especially in drug-addiction domain. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of our developed system AutoOPU in opioid user detection by comparisons with other alternative methods.
Collision Avoidance in Wake-Up Radio Enabled WSNs: Protocol and Performance Evaluation

In wake-up radio (WuR) enabled wireless sensor networks (WSNs), the envisaged application scenarios are primarily targeted at low trafﬁc load conditions. When applying WuR to medium or heavy trafﬁc load scenarios, however, collisions among wake-up calls (WuCs) may happen, resulting in a lower packet delivery ratio (PDR). In this paper, we propose a media access control protocol for WuR- enabled WSN that is capable of avoiding WuC collisions by activating a contention-based collision avoidance mechanism for WuC transmissions. The performance of the proposed protocol is evaluated by a Markov chain based mathematical model and is compared with a WuR protocol that performs only clear channel assessment for WuC transmissions. Numerical results demonstrate that our proposed protocol achieves better performance in terms of both PDR and network throughput.
Modeling Sentiment Association in Discourse for Humor Recognition

Humor is one of the most attractive parts in human communication. However, automatically recognizing humor in text is challenging due to the complex characteristics of humor. This paper proposes to model sentiment association between discourse units to indicate how the punchline breaks the expectation of the setup. We found that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. On the perspective of using sentiment related features, sentiment association in discourse is more useful than counting the number of emotional words.
0.6 V, 116 nW Neural Spike Acquisition IC with Self-Biased Instrumentation Amplifier and Analog Spike Extraction

This paper presents an ultralow power 0.6 V 116 nW neural spike acquisition integrated circuit with analog spike extraction. To reduce power consumption, an ultralow power self-biased current-balanced instrumentation amplifier (IA) is proposed. The passive RC lowpass filter in the amplifier acts as both DC servo loop and self-bias circuit. The spike detector, based on an analog nonlinear energy operator consisting of a low-voltage open-loop differentiator and an open-loop gate-bulk input multiplier, is designed to emphasize the high frequency spike components nonlinearly. To reduce the spike detection error, the adjacent spike merger is also proposed. The proposed circuit achieves a low IA current consumption of 46.4 nA at 0.6 V, noise efficiency factor (NEF) of 1.81, the bandwidth from 102 Hz to 1.94 kHz, the input referred noise of 9.37 μVrms, and overall power consumption of 116 nW at 0.6 V. The proposed circuit can be used in the ultralow power spike pulses acquisition applications, including the neurofeedback systems on peripheral nerves with low neuron density.
Is the incremental theory of intelligence a key to students’ motivational engagement? The moderating effects of self-enhancement and self-criticism

ABSTRACT The incremental theory of intelligence has been identified as a strong predictor of students’ learning motivation. Recent research has suggested various moderators of its effect. The present study sought to examine the moderating effects of self-enhancement and self-criticism on the relation between incremental intelligence beliefs and students’ motivational engagement. Responses were collected from 440 junior secondary school students in Macau. The results showed that self-enhancement strengthened the effect of incremental beliefs on students’ efforts after a perceived failure. Self-criticism strengthened the effect of incremental beliefs on efforts after a perceived success. The reasons for these findings are discussed and their pedagogical implications elucidated.
Text Completion using Context-Integrated Dependency Parsing

Incomplete linguistic input, i.e. due to a noisy environment, is one of the challenges that a successful communication system has to deal with. In this paper, we study text completion with a data set composed of sentences with gaps where a successful completion cannot be achieved through a uni-modal (language-based) approach. We present a solution based on a context-integrating dependency parser incorporating an additional non-linguistic modality. An incompleteness in one channel is compensated by information from another one and the parser learns the association between the two modalities from a multiple level knowledge representation. We examined several model variations by adjusting the degree of influence of different modalities in the decision making on possible filler words and their exact reference to a non-linguistic context element. Our model is able to fill the gap with 95.4% word and 95.2% exact reference accuracy hence the successful prediction can be achieved not only on the word level (such as mug) but also with respect to the correct identification of its context reference (such as mug 2 among several mug instances).
Fluid Flow Pinched by Near-Wall Bingham Plastic Zones: Simulation Study

Computational Fluid Dynamics tools and methods offer means for analyzing engineering and research concepts whose performance can be evaluated and understood with a reasonable accuracy. This study concerns the steady-state planar flow of a fluid pinched by (semi-solidified) Bingham plastic zones. The contractions limit the fluid flow and contribute to a pressure drop across the semi-orifice feature in the channel. Hence the authors study various flow scenarios and zone configurations in the channel with respect to the yield stress level, size and inlet velocity. The simulated results are presented as plots of pressure distribution in the channel and velocity profiles as well as graphs of pressure drop vs average flow velocity.
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks

Deep neural networks have led to a series of breakthroughs, dramatically improving the state-of-the-art in many domains. The techniques driving these advances, however, lack a formal method to account for model uncertainty. While the Bayesian approach to learning provides a solid theoretical framework to handle uncertainty, inference in Bayesian-inspired deep neural networks is difficult. In this paper, we provide a practical approach to Bayesian learning that relies on a regularization technique found in nearly every modern network, \textit{batch normalization}. We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models, and we demonstrate how this finding allows us to make useful estimates of the model uncertainty. With our approach, it is possible to make meaningful uncertainty estimates using conventional architectures without modifying the network or the training procedure. Our approach is thoroughly validated in a series of empirical experiments on different tasks and using various measures, outperforming baselines with strong statistical significance and displaying competitive performance with other recent Bayesian approaches.