Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Detection Using Pattern-of-Life Visual Metaphors

Complex dependencies exist across the technology estate, users and purposes of machines. This can make it difficult to efficiently detect attacks. Visualization to date is mainly used to communicate patterns of raw logs, or to visualize the output of detection systems. In this paper we explore a novel approach to presenting cybersecurity-related information to analysts. Specifically, we investigate the feasibility of using visualizations to make analysts become anomaly detectors using Pattern-of-Life Visual Metaphors. Unlike glyph metaphors, the visualizations themselves (rather than any single visual variable on screen) transform complex systems into simpler ones using different mapping strategies. We postulate that such mapping strategies can yield new, meaningful ways to showing anomalies in a manner that can be easily identified by analysts. We present a classification system to describe machine and human activities on a host machine, a strategy to map machine dependencies and activities to a metaphor. We then present two examples, each with three attack scenarios, running data generated from attacks that affect confidentiality, integrity and availability of machines. Finally, we present three in-depth use-case studies to assess feasibility (i.e. can this general approach be used to detect anomalies in systems? ), usability and detection abilities of our approach. Our findings suggest that our general approach is easy to use to detect anomalies in complex systems, but the type of metaphor has an impact on user’s ability to detect anomalies. Similar to other anomaly-detection techniques, false positives do exist in our general approach as well. Future work will need to investigate optimal mapping strategies, other metaphors, and examine how our approach compares to and can complement existing techniques.
When Tags ‘Read’ Each Other: Enabling Low-cost and Convenient Tag Mutual Identification

Though being widely used in industrial and logistic applications, current passive RFID technology still has a fundamental limitation: Individual users, who do not carry any reader, are difficult to interact with tagged items, such as retrieving their digital profiles and requesting certain association with them. Recent proposals to improve user-item interaction experience rely on special hardware such as smartphone based RID scanner. This work presents a promising approach to allow each user to interact with tagged item using only one passive tag, which is named Tag Mutual Identification Interface (TagMii). TagMii requires a user to put her user tag in a physical proximity with an item tag to express certain interactions between the user and item. The key idea behind TagMii is to utilize two experimental observations: 1) inductive coupling for detecting interaction events, and 2) channel similarity for determining the actual interacting tags. We implement TagMii using commodity off-the-shelf RID devices and conduct experiments in complex environments with rich multipath, mobility, wireless signals, electrical devices, and magnetic fields. The results show that TagMii provides accurate mutual identification. TagMii is a completely new approach for user-item interactions in pervasive environments and enables many user-friendly IoT applications with low cost and convenience.
Faster energy maximization for faster maximum flow

In this paper we provide an algorithm which given any m-edge n-vertex directed graph with integer capacities at most U computes a maximum s-t flow for any vertices s and t in m 11/8+o(1) U 1/4 time with high probability. This running time improves upon the previous best of Õ(m 10/7 U 1/7) (Mądry 2016), Õ(m √n logU) (Lee Sidford 2014), and O(mn) (Orlin 2013) when the graph is not too dense or has large capacities. We achieve this result by leveraging recent advances in solving undirected flow problems on graphs. We show that in the maximum flow framework of (Mądry 2016) the problem of optimizing the amount of perturbation of the central path needed to maximize energy and thereby reduce congestion can be efficiently reduced to a smoothed ℓ2-ℓ p flow optimization problem, which can be solved approximately via recent work (Kyng, Peng, Sachdeva, Wang 2019). Leveraging this new primitive, we provide a new interior point method for maximum flow with faster convergence and simpler analysis that no longer needs global potential functions involving energy as in previous methods (Mądry 2013, Mądry 2016).
Parallel and External High Quality Graph Partitioning

Partitioning graphs into k blocks of roughly equal size such that few edges run between the blocks is a key tool for processing and analyzing large complex real-world networks. The graph partitioning problem has multiple practical applications in parallel and distributed computations, data storage, image processing, VLSI physical design and many more. Furthermore, recently, size, variety, and structural complexity of realworld networks has grown dramatically. Therefore, there is a demand for efficient graph partitioning algorithms that fully utilize computational power and memory capacity of modern machines. A popular and successful heuristic to compute a high-quality partitions of large networks in reasonable time is multi-level graph partitioning approach which contracts the graph preserving its structure and then partitions it using a complex graph partitioning algorithm. Specifically, the multi-level graph partitioning approach consists of three main phases: coarsening, initial partitioning, and uncoarsening. During the coarsening phase, the graph is recursively contracted preserving its structure and properties until it is small enough to compute its initial partition during the initial partitioning phase. Afterwards, during the uncoarsening phase the partition of the contracted graph is projected onto the original graph and refined using, for example, local search. Most of the research on heuristical graph partitioning focuses on sequential algorithms or parallel algorithms in the distributed memory model. Unfortunately, previous approaches to graph partitioning are not able to process large networks and rarely take in into account several aspects of modern computational machines. Specifically, the amount of cores per chip grows each year as well as the price of RAM reduces slower than the real-world graphs grow. Since HDDs and SSDs are 50 – 400 times cheaper than RAM, external memory makes it possible to process large real-world graphs for a reasonable price. Therefore, in order to better utilize contemporary computational machines, we develop efficient multi-level graph partitioning algorithms for the shared-memory and the external memory models. First, we present an approach to shared-memory parallel multi-level graph partitioning that guarantees balanced solutions, shows high speed-ups for a variety of large graphs and yields very good quality independently of the number of cores used. Important ingredients include parallel label propagation for both coarsening and uncoarsening, parallel initial partitioning, a simple yet effective approach to parallel localized local
A Neural Document Language Modeling Framework for Spoken Document Retrieval

Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answering, information retrieval and so on. In the context of natural language processing (NLP), language representations learned by referring to autoregressive language modeling or autoencoding have shown giant successes in many downstream tasks, so the school of studies have become a major stream of research recently. Because the immenseness of multimedia data along with speech have spread around the world in our daily life, spoken document retrieval (SDR), which aims at retrieving relevant multimedia contents to satisfy users’ queries, has become an important research subject in the past decades. Targeting on enhancing the SDR performance, the paper concentrates on proposing a neural retrieval framework, which assembles the merits of using language modeling (LM) mechanism in SDR and leveraging the abstractive information learned by the language representation models. Consequently, to our knowledge, this is a pioneer study on supervised training of a neural LM-based SDR framework, especially combined with the pretrained language representation methods. A series of empirical SDR experiments conducted on a benchmark collection demonstrate the good efficacy of the proposed framework, compared to several existing strong baseline systems.
Machine-Learning Based Relay Selection in AF Cooperative Networks

With the significant increase of wireless network nodes and traffic load in recent years, especially in the emerging internet-of-things (IoT) and vehicular networks, the design of a fast adaptive relay selection algorithm that is able to cope with a quickly changing environment became a necessity. In particular, the problem of multiple relay selection and beamforming under individual power constraints is investigated in this paper when the amplify-and-forward protocol is used to forward the data to the destination. The proposed algorithm first performs relay selection and beamforming using iterative convex optimization. The selection decisions are stored and processed before being used by a proposed multi-agent machine-learning (ML) model to imitate with high accuracy the optimal selection decision in real time with much less computational complexity. Simulation results confirm that the performance of the proposed technique is very close to the exhaustive search (ES) and to well known algorithms but with an execution time that is thousands of times shorter than traditional techniques.
Effect of prepartum supplementation of yeast culture (Saccharomyces cerevisiae) on biochemical parameters of dairy cows and their newborn calves.

FROHDEOVA MARTINA, MLEJNKOVA VERONIKA, LUKESOVA KATEŘINA, DOLEŽAL PETR. 2014. Eff ect of Prepartum Supplementation of Yeast Culture (Saccharomyces Cerevisiae) on Biochemical Parameters of Dairy Cows and Their Newborn Calves. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 62(5): 897–904. The aim of our experiment was to compare the eff ect of diff erent levels of the addition of Saccharomyces cerevisiae (S. c.) yeast culture on feed carrier to the current ration MK DOJ Levucell SC 20, Saccharomyces cerevisiae CNCM I-1077 (E 1711) 20.1010 CFU on the blood parameters of high-pregnant breeding cows and their calves. The experiment included 42 breeding cows of the Czech Fleckvieh cattle breed and their calves. The breeding cows were divided into two age groups, each of 21 heads. The fi rst group included heifers and the second group consisted of cows on the 2nd and higher lactation. Each age group had 7 control animals (Heifers/Cows – control), 7 animals receiving 50 g of yeast culture on the feed carrier per head and day (Heifers/Cows – 50 g), and 7 animals receiving 150 g of yeast culture on the feed carrier per head and day (Heifers/Cows – 150 g). Blood of the animals was sampled three times during the experiment – two times in the cows and one time in their calves. The fi rst blood sample was taken from the breeding cows before the start of feeding the yeast culture ca. 23 days before the expected parturition (Cow −23). The second blood sample was taken from the breeding cows within 24 hours a er birth (Cow +2) and the third blood sample was taken from the calves on the 3rd–4th day a er birth (Calf +4). Parameters ascertained in the processed serum were: immunoglobulines G (IgG), crude protein (CP), gamma glutamyl-transferase (GGT), urea, glutamate-pyruvate-transaminase (GPT) and glutamate-oxaloacetate-transaminase (GOT). Results of our experiment, which lasted from 16.3 to 28.6 (105 days) showed IgG in the serum of cows on the 2nd and higher lactation in the control group (3.8 ± 1.48 mg/ml) was statistically signifi cantly lower (P < 0.05) in the blood sample Cow +2 than in the serum of cows in the 2nd and higher lactation fed with the lower concentration of Saccharomyces cerevisiae at the same sampling (15.9 ± 11.41 mg/ml). This correlates also with the fact that the content of IgG antibodies in the serum of calves a er the cows on the 2nd and higher lactation in the control group (3.9 ± 2.06 mg/ml) was statistically signifi cantly (P < 0.05) lower than that of calves a er the cows on the 2nd and higher lactation with the lower concentration of Saccharomyces cerevisiae (14.6 ± 8.67 mg/ ml). As to the higher addition, no statistically signifi cant diff erence of the eff ect on the IgG content was recorded (P < 0.05).
Cloning of Two Dehydrin Genes from The Halophytes of The Egyptian Northwest Coastal Region

Throughout their life cycle, plants are subjected to many adverse environmental stresses such as drought, high temperatures, etc. that dramatically affect plant survival and reduce productivity. To cope with such stresses, plants produce several stress-induced proteins that play a definite role in protecting plants during such severe conditions (Muthalif and Rowland, 1994). The late embryonic stage represents one of the abiotic stress conditions where the seed starts to lose water content during desiccation. Several molecules have been found to play vital roles in seed development and are thought to help in saving the embryos during desiccation. These include sugars (Koster and Leopold, 1988; Chen and Burris, 1990) and proteins, among which are the late-embryogenesis abundant proteins (LEA) (Blackman et al., 1995; Dure, 1993; Close, 1996; Ingram and Bartels, 1996). LEA proteins were found in the seeds of several plants and in vegetative organs, especially under abiotic stress conditions (Thomashow, 1999; Hundertmark and Hincha, 2008). According to amino acid sequence homology, LEA proteins have been separated into different groups (Hundertmark and Hincha, 2008). From these groups the LEA D11 family (LEA type 2 proteins), also known as Dehydrins (Close, 1997), have been estimated to comprise up to 4% of the total seed protein. (Wise and Tunnacliffe, 2004). Expression of the Dehydrin proteins have been found to be associated with the protection of various types of plant cells from osmotic stresses, such as those caused by desiccation, salt, and low temperatures (Skriver and Mundy, 1990; Close, 1996; Ingram and Bartels, 1996; Allagulova et al., 2006). Hyperosmotic conditions and low temperatures cause cellular dehydration, resulting in the reduction of cytosolic volumes and the alteration of cellular mechanisms. Toward survival, plants accumulate Dehydrin proteins during these conditions in the dehydrating plant tissue, (Abba et al., 2006). Several studies revealed that Dehydrins are widely distributed in the plant kingdom (Abba et al., 2006), in brown algae (Li et al., 1998), in lichen Selaginella lepidophylla (Close, 1997) and in cyanobacterium (Close et al., 1993).
On the Thin Film Approximation for the Flow of a Viscous Incompressible Fluid down an Inclined Plane

We consider a two-dimensional motion of a thin film flowing down an inclined plane under the influence of the gravity and the surface tension. In order to investigate the stability of such flow, it is hard to treat the Navier--Stokes equations directly, so that a thin film approximation is often used. It is an approximation obtained by the perturbation expansion with respect to the aspect ratio $\delta$ of the film under the thin film regime $\delta\ll1$. Our purpose is to give a mathematically rigorous justification of the thin film approximation by establishing an error estimate between the solution of the Navier--Stokes equations and those of approximate equations. To this end, in this paper we derive a uniform estimate for the solution of the Navier--Stokes equations with respect to $\delta$ under appropriate assumptions.
Creating a Multilingual Terminological Resource using Linked Data: the case of Archaeological Domain in the Italian language

We want to thank SmartApps for providing useful material and information for the realization of this project. This project has been partially supported by the PON Ricerca e Innovazione 2014/20 and the POR Campania FSE 2014/2020 funds. Sina Ahmadi is also supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731015.
Effect of endurance exercise training on aflatoxin B1 hepatotoxicity in rats

Effect of endurance exercise training on hepatotoxicity induced by aflatoxin B1 (AFB1) was studied in rats. Rats were subjected to swimming with 1%BW resistance for 30min, 5 days/week for 14 weeks before administration of AFB1. Endurance exercise training induced high physical fitness as shown by reduction in resting heart rate and increase in the activities of mitochondrial succinate dehydrogenase and citrate synthase in the gastrocnemius muscle. Water-immersed rats had similar basal physical fitness when compared with that of the untrained rats. Endurance exercise training as per the above schedule followed by a single i.p. injection of AFB1 (2mg/kg BW) caused a significant increase in the activities of serum alanine aminotransferase (ALT) by 6.6 fold and aspartate aminotransferase (AST) by 1.8 fold and increased the severity of histopathologic hepatic necrosis at 24h after AFB1 administration. Endurance exercise training potentiated AFB1-induced hepatotoxicity by increasing the activity of the hepatic monooxygenase enzymes aniline hydroxylase and p-nitroanisole-O-demethylase. These results suggest that potentiation of AFB1-hepatotoxicity by endurance exercise training may be due to an increase in the metabolic formation of AFB1-8, 9-oxide, which, in turn, causes a marked increase in AFB1 binding to hepatic DNA and proteins.
Decoupled Uplink-Downlink User Association in Ultra-Dense Networks: A Contract-Theoretic Approach

User association is a crucial factor that affects the performance of wireless networks. In current cellular networks, user association is typically coupled, which means an user equipment (UE) must associate with the same base station (BS) in uplink (UL) and downlink (DL). For single-tier wireless networks, such mechanism is simple and effective. However, in heterogeneous ultra-dense networks (UDNs), there are distinct differences in transmission power, data traffic and channel quality etc., for which coupled association could restrict the performance of system. To cope with it, the concept of decoupled UL-DL (DUDe) association has been introduced recently, which enables a UE to associate with different BSs in UL and DL. In this paper, we investigate decoupled UL-DL user association in UDNs. Considering the existence of asymmetric information (i.e., channel gains and intercell interferences), which can be seen as the private information for UE, we propose a contract-theoretic user association approach. Particularly, we model the decoupled association process as a monopoly labor market, where BSs act as employers and offer contracts to employees (i.e., UEs). The contract items cover the available associated bandwidths, transmitted powers and corresponding prices. Then BS broadcasts these drafted contract information, and UE selects to sign the optimal contract by considering her own demands. Numerical results show significant superiorities of DUDe than coupled UL-DL association in perspective of nodes utilities and social surplus, and compared with the existing user association methods, contract-theoretic approach shows a certain improvement in performance.
On the Redundancy Detection in Keyframe-Based SLAM

Egomotion and scene estimation is a key component in automating robot navigation, as well as in virtual reality applications for mobile phones or head-mounted displays. It is well known, however, that with long exploratory trajectories and multi-session mapping for long-term autonomy or collaborative applications, the maintenance of the ever-increasing size of these maps quickly becomes a bottleneck. With the explosion of data resulting in increasing runtime of the optimization algorithms ensuring the accuracy of the Simultaneous Localization And Mapping (SLAM) estimates, the large quantity of collected experiences is imposing hard limits on the scalability of such techniques. Considering the keyframe-based paradigm of SLAM techniques, this paper investigates the redundancy inherent in SLAM maps, by quantifying the information of different experiences of the scene as encoded in keyframes. Here we propose and evaluate different information-theoretic and heuristic metrics to remove dispensable scene measurements with minimal impact on the accuracy of the SLAM estimates. Evaluating the proposed metrics in two state-of-the-art centralized collaborative SLAM systems, we provide our key insights into how to identify redundancy in keyframe-based SLAM.
TCP throughput characteristics over 5G millimeterwave network in indoor train station

To realize highly reliable video surveillance and provide ultrahigh-definition/immersive video streaming, it is planned to adopt the 5G cellular system using millimeter-wave (mmWave) as the wireless-network infrastructure. However, mmWave communication has a challenging issue: mmWave communication is extremely sensitive to obstacles, such as walls, pillars, and even human bodies, and this issue easily increases the packet loss rates and round trip time (RTT) (or disconnection from the base station) due to a no line of sight (NLOS) environment. Therefore, in this work, 5G throughput performances were evaluates in an indoor train station by considering the effect of an NLOS environment caused by blockage by human bodies. In addition, to improve the robustness of TCP transmission in a high-RTT and high-packet-loss environment (e.g., an NLOS environment), a state-of-the-art TCP, TCP-FSO, was used. In the evaluations, the MATLAB 5G library was used to simulate the 5G environment, and a Linux software-based network emulator, Traffic Control, was used to emulate the 5G network. From the evaluations, it the 5G mobile throughput characteristics were confirmed in three different crowded patterns (low, middle, and high density), and the TCP-FSO advantage against CUBIC-TCP was validated.
Preparation and functional characterisation of fish skin gelatin and comparison with commercial gelatin

Summary Gelatin was extracted from the skin of farmed giant catfish (GC) and tilapia (TP) at a yield of 19.50% and 23.34% (wet wt). It was high in protein (84–88%) but low in fat (0.09–1.24%) and ash content (0.15–0.17%). The GC exhibited lower emulsifying activity (24–35%), but greater foam ability (98–110%), water holding capacity (477–844%) and fat binding capacity (2541–3314%) than commercial beef skin gelatin (BF) (P < 0.05). GC and TP showed comparable functional properties to BF. SDS-PAGE patterns of TP gelatin showed high band intensity for the α- and β-components, while the lowest band intensity of the major component was found in the BF. From the study, it can be concluded that the farmed freshwater fish skin GC and TP is a prospective source for producing a significant gelatin yield with desirable functionalities. Because of these, fish skin gelatin could be more effectively and widely used in food industries as a good food ingredient.