Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Multi-Pair Active Friending in Online Social Networks

In recent years, friending recommendation has played a crucial role on the explosive growth of online social networks. Prior studies on this problem have the following two limitations: i) they assume that there is only one pair of source and target at one time; and ii) the intermediate user can indefinitely forward friending requests. These two assumptions are impractical for online social networks. To bridge this gap, we study how to maximize the acceptance probability for multi-pair active friending in online social networks. In this paper, we first establish a probability model for the Multi-pair Active Friending with Tolerance and then propose two efficient algorithms: Multi-pair Active Friending algorithm for Conflict Resolution (MAFCR) and Group-based Multi-pair Active Friending algorithm for Conflict Resolution (MAFCR-G). Through extensive experiments, we show that the algorithm MAFCR significantly outperforms the baseline algorithm in terms of average acceptance probability, and the algorithm MAFCR-G greatly reduces the running time while obtaining similar average acceptance probability.
Self-Calibration for Massive MIMO with Channel Reciprocity and Channel Estimation Errors

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.
ChainSecure - A Scalable and Proactive Solution for Protecting Blockchain Applications Using SDN

Nowadays, blockchain is seen as one of the main technological innovations. Many applications can rely on the blockchain to secure their exchanges. However, applications with private interest cannot rely on public blockchains. First, in a public blockchain, anyone can read the whole data of the blockchain. Second, anyone can participate to the "consensus process"; the process for determining the validity of each transaction. Consortium and fully private blockchains aim to combine forcefulness of blockchains with controlled consensus process and stricter permissions for deploying a node and joining the blockchain network. In both consortium and fully private blockchains, the number of peers on the blockchain network is very small in comparison with public blockchain. Nonetheless, by targeting the nodes of blockchains, an attacker can easily manage the whole blockchain and takes control of the consensus process to validate his illegitimate transactions. In this paper, to defend blockchain nodes from DNS amplification attacks, we propose a scalable and proactive solution in the context of software defined networks (SDN), named ChainSecure. ChainSecure consists of 3 schemes: (1) StateMap, a novel stateful mapping scheme (SMS) to perform a mapping one-to-one between DNS request and response; (2) Entropy calculation scheme (ECS) to measure the disorder &#x002F; randomness of data using sFlow in order to detect illegitimate flows; (3) DNS DDoS Mitigation (DDM) module to effectively mitigate illegitimate DNS requests. The experimental results show that ChainSecure protects blockchain nodes and can detect&#x002F;mitigate the attack quickly to achieve high accuracy in detecting illegitimate DNS traffic making it a promising solution to protect blockchain nodes from DNS amplification attacks.
Base Station MIMO Detector Algorithm Implementations

In this paper, we implement a high throughput multiple-input multiple-output (MIMO) detector for single-carrier frequency-division multiple access (SC-FDMA) base station. High-level Synthesis (HLS) tool is used for implementing the algorithm on Xilinx Virtex and Zynq FPGAs.First, we compare the throughput performance and power consumption results of the different implementations. Second, we evaluate the quality of the results by comparing the HLS results to handwritten register-transfer level (RTL) implementations. In conclusion, the HLS tools have evolved into applicable implementation tools. Furthermore, the possible slight losses in the performance or design complexity with the HLS design method could be counteracted by choosing a higher category FPGA.
Downlink Lattice-Partition-Based Non-Orthogonal Multiple Access without SIC for Slow Fading Channels

In this paper, we develop a lattice-partition- based downlink non-orthogonal multiple access (NOMA) scheme for slow fading channels without successive interference cancellation (SIC) at the receivers. With the knowledge of statistical channel state information at the transmitter, our scheme uses a finite constellation drawn from an $n$-dimensional lattice and employs channel coding on top of it. The outage rates achieved by our scheme without SIC are analyzed and their gaps to the multiuser outage capacity are derived. We show, both theoretically and numerically, that our scheme without SIC is capable of approaching any point in the multiuser outage capacity region within a constant gap when the required outage probability is smaller than 63.21%, which covers almost all cases of practical interest. Simulation results based on various lattices are provided and demonstrate that the near-capacity performance can be attained by our NOMA scheme without SIC.
Resource Pooling in Multipath Congestion Control for Content Centric Networks

Resource Pooling concept has been proposed as fair sharing of total network resources among all users sharing a network so that a whole network is treated as a single pooled resource. MPTCP (Multipath TCP) has been proposed as one of the most promising congestion controls which realize the Resource Pooling in the situation that each user can utilize multiple paths. In CCN (Content-Centric Networking), several multipath/multisource congestion control methods have been proposed. A fundamental difference between CCN congestion control and MPTCP where single source is assumed, is multisource and the Resource Pooling in multipath/multisource situation is quite a new research issue. In this paper, we discuss congestion control for CCN from the viewpoint of the Resource Pooling and show that our previously proposed congestion control satisfies the Resource Pooling concept. Our simulation results also show that our proposed congestion control achieves macroscopic fairness among users, which means the Resource Pooling concept is satisfied.
A Hybrid SDN Path Computation for Scaling Data Centers Networks

Initial SDN deployments endorsed a centralized architecture with a single controller that is responsible for managing entire networks. Yet, this scheme has proven to be unscalable and unreliable. Though distributed control plane architectures have gained ground recently, they bring new concerns. A principal question that should be investigated thoroughly is how to efficiently perform path computation and update flow tables in very large SDNs. In this paper, we address this problem by proposing ScalCon, a scalable hybrid SDN architecture that distributes the control plane over different controller types (i.e., Fog, Edge, Cloud). ScalCon distributes the load of path computation among all controllers and uses a multi-priority queening model and node parallelism to cut down the synchronization overhead. Simulation results show that ScalCon outperforms existing schemes in terms of path computation time, path setup latency. end-to-end delay and communication overhead.
Onboard Compensation of Linear and Non-Linear Hardware Imperfections in Multiport Amplifiers

Future high throughput satellites (HTS) aim at providing ultra high data rates with flexible payloads. The multiport amplifier (MPA) is an important flexible payload technology that provides efficient use and allocation of the radio frequency (RF) power. However, several practical limitations affect their performance. This paper outlines general design principles and mathematical models for the MPAs to highlight different practical scenarios which can lead to system performance degradation. Two major issues detailed in this paper are the hardware imperfections in implementing input/output-hybrid Butler matrices (INET/ONET) and the non-linear effects of high power amplifiers (HPAs). The paper also presents a two-step adaptive technique to improve the overall MPA performance, where in the first step a novel onboard compensation technique for the imperfect INET/ONET is presented. Furthermore, the high power amplifiers (HPAs) in the MPA are digitally predistorted to reduce the intermodulation noise and spectral regrowth in the second step. Simulation results indicate that by implementing the proposed two-step solution, there is not only a gain in the bit error ratio (BER) performance, but also in system robustness against hardware imperfections and aging effects due to the technique's adaptive nature.
Learning to Cooperate in Decentralized Wireless Networks

Several key wireless communication setups call for coordination capabilities between otherwise interfering transmitters. Coordination or cooperation can be achieved at the expense of channel state information exchange. When such information is noisy, the derivation of robust decision-making algorithms is unfortunately known to be very challenging via conventional optimization method. In this paper we introduce a learning-based framework which allows the agents, aka. the transmitters, to produce as-relevant-as-possible messages to each other on the basis of arbitrarily partial and noisy local channel state information. The messages are produced via distributed deep neural networks (DNNs) which are trained for a specific coordination purpose. The message-passing DNNs are completed with decision-making DNNs which are trained for a network metric maximization. Promising preliminary results are obtained in the context of sum-rate maximizing decentralized power control.
On the Tradeoff Between Data-Privacy and Utility for Data Publishing

A typical method for privacy-preserving data publishing mechanism is to add random noise to the original data for publishing. No matter what kind of noise is added, there is a chance that the original state can be estimated in a certain accuracy. The probability of the original data inferred by the malicious receiver in a given interval is measured by $(\alpha,\ \beta)$ -data-privacy. With random noise added to the original data, the utility of the published data will decrease. In this paper, we investigate the tradeoff between data privacy and data utility under $(\alpha,\beta)$ - data-privacy, aiming to seek an optimal noise distribution. To maximize the weighted sum of privacy and utility we prove that when the added noise is symmetric and the data utility is measured by $\ell^{1}$ - or $\ell^2$ -norm function, the optimal noise follows the uniform distribution. Then we further investigate the optimal noise to maximize data utility with a certain privacy guarantee and we derive that the optimal noise is a group of impulse functions. Finally, we compare $(\alpha, \beta)$ -data-privacy with differential privacy and obtain the inequality relationship between the two privacy parameters. Simulations are conducted to validate the correctness of the obtained results.
Safe Temporal Planning for Urban Driving

A self-driving car must always have a plan for safely coming to a halt. Often, finding these safe plans is treated as an afterthought. In this paper, we demonstrate that techniques explicitly designed for safety can yield higher quality plans and lower latency than conventional planners in an urban driving setting. We adopt ideas from a previously-proposed safe online real-time heuristic search method to the spatiotemporal state lattices used when planning for autonomous driving. We experimentally compare our proof-of-concept implementation to conventional methods and find significantly improved performance while still maintaining passenger com-
The effects of biochar on the soil properties and on yield formation of pea (Pisum sativum L) nine years after application

Tiedekunta/Osasto Fakultet/Sektion – Faculty Agriculture and Forestry Laitos/Institution– Department Agricultural Sciences Tekijä/Författare – Author Samuel Amoah Työn nimi / Arbetets titel – Title The effects of biochar on the soil properties and on yield formation of pea (Pisum sativum L) nine years after application Oppiaine /Läroämne – Subject Plant production sciences Työn laji/Arbetets art – Level Masters degree Aika/Datum – Month and year May 2019 Sivumäärä/ Sidoantal – Number of pages 46 Tiivistelmä/Referat – Abstract Biochars, made from biomass heated in limited oxygen, have been suggested as a sustainable means of increasing crop productivity. Two of the most commonly reported benefits of biochars are improved soil water availability and nutrient status, due of which also yield increases have been reported. Most studies so far have focused on subtropical soils that are low in initial carbon content, and cereals have been the main crops studied. There is also lack of knowledge of the effects of biochar in longer term than five years on the yield formation of grain legumes like peas. A long-term field experiment was conducted in Helsinki, Finland to investigate the effects of softwood biochar on the soil properties and on the yield formation of peas. Three levels of biochar rates were used: 0 ton/ha, 5 ton/ha and 10 ton/ha in conjunction with 3 NPK fertilizer levels of 30, 65, and 100 percent of the recommended levels. The addition of biochar was tied to slightly elevated levels of soil moisture at the upper soil layers (0 – 18 cm). This increase was however not significant (p > 0.05). Changes in biochar porosity over the years may have led to decreased water holding capacity of the soil and hence low moisture content. The soil nutrient status was also not significantly affected by biochar additions, except for sulphur levels which recorded a marginal significance of p < 0.1. Changes in biochar properties over time could also be responsible for the lack of effects on soil nutrients. The soil used was relatively fertile (3.5 % C), hence the effects of biochar were insignificant. Fertilizer effects were also not significant, except for significant levels of such nutrient as P, Ca, P and S. The lack of fertilizer effects could be due to the relatively fertile nature of the Luvic Stagnosol soil. The lack of effects of biochar on soil properties resulted in non-significant results for yield components of peas. The relatively dry weather during the growing season could also be responsible for the vast lack of significance recorded. Avainsanat – Nyckelord –
Sinusoidal Parameter Estimation from Signed Measurements Obtained via Time-Varying Thresholds

We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with time-varying thresholds. In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function. However, 1bRELAX can only be used in applications involving a small number of sinusoids due to the time-consuming exhaustive search procedure needed in each iteration. In this paper, we present a majorization-minimization (MM) based 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational efficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the likelihood function iteratively using simple FFT operations to reduce the computational cost of 1bRELAX while maintaining its excellent estimation accuracy. Numerical examples are presented to demonstrate the effectiveness of the proposed method.
EmotionPush: Emotion and Response Time Prediction Towards Human-Like Chatbots

Recently chatbots have been widely deployed in social network messengers. To make these chatbots more human, we present EmotionPush, the first social spoken-language private dialog dataset containing instant message logs and the unique read event logs, containing a total of 162,031 message logs and their corresponding read logs of real private conversations on Facebook Messenger. We ensure data privacy by masking all the named entities with a code composed by their type and an unique ID. In addition, we take care of the debug need by releasing messages partially by their original words and partially in the form of word embeddings. In addition, with this dataset, we propose the emotion classification task and a novel response-time prediction task to enhance the humanity of chatbots. We establish strong baselines for these two tasks. Experiment results show that EmotionPush helps to achieve over 90% accuracy for major emotion classification and 89% accuracy for response time prediction. We expect to enable chatbots to know when to respond and what message to send to encourage user responses.
A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions

In this paper, we perform a comparative evaluation of off-the-shelf embedding models over the task of compositionality prediction of multiword expressions("MWEs"). Our experimental results suggest that character- and document-level models capture knowledge of MWE compositionality and are effective in modelling varying levels of compositionality, with the advantage over word-level models that they do not require token-level identification of MWEs in the training corpus.
Generalized Beamspace Modulation for mmWave MIMO

As a recently emerging technology, index-based modulation (IBM) has been attracting increasing research interests for its improved bit error rate (BER) performance and power efficiency. At present, the applications of two typical schemes named spatial modulation (SM) and subcarrier index modulation (IM), as well as their variants are introduced to microwave systems. To make IBM applicable in mmWave systems, the special properties of channel environments and system architectures should be taken into account. In this paper, we present a novel IBM scheme termed as generalized beamspace modulation (GBM) for mmWave beamspace multiple-input multiple-output (MIMO) systems. Unlike the frequency or spatial domain in which the existing IBM schemes are typically performed, GBM is implemented in the beamspace. To achieve near- optimal BER performance in GBM systems, a general effective beamspace channel (EBC) optimization method is derived based on the minimum asymptotic pairwise error probability (APEP) criterion. The optimal maximum-likelihood (ML) detector and the lowcomplexity detector are both provided. Thanks to our proposed GBM scheme, the BER performance can be noticeably enhanced compared to plain mmWave systems, with a smaller number of active frequency chains are used during transmission.
Effects of Different Metal Ions on the Stability of Anthocyanins as Indicators

In this paper, the stability of anthocyanins in morning glory in the presence of different ions was analyzed by spectrophotometry. The anthocyanin extract was obtained by heating and reflux. Then the maximum absorption wavelength of anthocyanin was obtained by specific characteristic absorbance of anthocyanin, and the concentration of anthocyanin was calculated. Six different metal ions were added to the anthocyanin solution to determine the influence of the stability of anthocyanin by measuring the absorbance value. Through the analysis, we obtained calcium ions which can promote the stability of anthocyanins. Finally, we find the best concentration ratio of calcium ion and anthocyanin. Through the study, we found that when the concentration of anthocyanin and calcium ion is 1:7, the best color retention effect is achieved.
Platelet-rich Plasma: Three Decades and Ongoing, Do We Have a Conclusion?

Blood components for non-transfusion uses have been the focus of researches in various clinical fields for the last thirty years. Platelet-rich plasma (PRP) can expedite healing, with minimal side effects. As a biologic autologous product, it is well-tolerated, and safe choice for many physicians and patients. Yet, no consensus has been reached regarding PRP preparation, needed volume, the use of an activator, number of sessions, and the period between the sessions in different clinical scenarios. Thus, further clinical studies are needed with higher level of evidence taking into consideration the optimization of these aspects. Up to date, there are many PRP preparation systems on the market with FDA clearance. Meanwhile, FDA has not attempted to regulate activated PRP. Determining the main bioactive components which are responsible for the clinical effects of PRP and the inter-individual variability of growth factors and cytokines production and the synergy of platelets count and growth factors will remain as major obstacles to achieve standardization of PRP. PRP is yet to become the standard of care; nevertheless, more clinical studies with longer periods of follow up to understand the best candidate and to determine the best ways to use it to improve healing; it may be an approved practice in the future.
User Association for Ultra Dense Networks with QoS Guarantees

We study the problem of user association in Ultra Dense Networks (UDNs) for two network services; one requiring QoS guarantees to VIP flows, and one best effort service. The goal is to take advantage of statistical multiplexing in order to optimize the use of resources, while ensuring that the VIP flows enjoy active performance guarantees. We formulate this as an optimization problem, show that the problem is convex, and finally demonstrate that the optimum point can in fact be realized by distributed user association rules. In this way, our framework uses the fundamental problem of user association to show that both isolation and statistical multiplexing can be achieved in the context of UDNs, when different services or slices must share BS resources, as envisioned in 5G networks. We demonstrate no violations of the VIP flow constraint on real data traces for mobile network traffic, while a baseline best effort distributed policy applied to this setup inflicts up to 46.5% violations.