Miracle-dz/UniFAR-FTUs huggingface dataset:
Alexithymia and the Reduced Ability to Represent the Value of Aversively Motivated Actions

Alexithymia is a subclinical trait defined by difficulties in identifying and describing feelings and a cognitive style avoidant of introspection. Extensive literature shows that alexithymia is characterized by multifaceted impairments in processing emotional stimuli. Nevertheless, the mechanisms that may account for such impairments remain elusive. Here, we hypothesize that alexithymia may be understood as impairment in learning the emotional value of one’s own actions and test this comparing performance of participants with high (HA) and low (LA) levels of alexithymia on a probabilistic selection task. Results show that, compared to LA, HA need more time to learn the value of individual stimuli and associated actions as difference in reinforcement rate between stimuli decreases. In addition, HA appear less able to generalize the value of previously learned actions that lead to a negative outcome, to make adaptive choices in a new context, requiring more time to avoid the most negative stimulus between two negative stimuli. Together, the results indicate that individuals with alexithymia show impaired learning of the value of aversively motivated actions. We argue that this impairment may hinder the construction of internal representations of emotional stimuli and actions and represent a mechanism that may account for the difficulties of alexithymia in processing emotional stimuli.
Therapeutic Effect of the Substrate-Selective COX-2 Inhibitor IMMA in the Animal Model of Chronic Constriction Injury

Enhancement of endocannabinoid signaling has emerged as an attractive strategy for the treatment of pain. In addition to the well-characterized hydrolytic pathways, cyclooxygenase-2 (COX-2) mediated oxygenation is thought to be an alternative route for endocannabinoid metabolism and therefore provides a new avenue for drug intervention. In this study, we examined the therapeutic effect of indomethacin morpholinamide (IMMA), a novel substrate-selective COX-2 inhibitor, in the chronic constriction injury (CCI) mouse model. Treatment with IMMA significantly alleviated hyperalgesia and mechanical allodynia demonstrated by increased thermal withdrawal latency in Hargreaves test and tactile thresholds in Von Frey test. Accumulation of astrocytes and microglia in spinal cord dorsal horn and infiltration of macrophages into the dorsal root ganglion and sciatic nerve were reduced by drug treatment. Co-administration of the CB2 receptor antagonist, but not the CB1 receptor antagonist partially reversed the inhibitory effect of IMMA on pain sensitivity and inflammatory infiltrates. IMMA downregulated the mRNA expression of TNF-α and IL-1β and the production of IL-6 and MCP-1 proteins in the ipsilateral sciatic nerve. The enhanced NF-κB DNA binding activity in the CCI mouse dorsal spinal cord was also significantly reduced, suggesting that inactivation of NF-κB contributes to the anti-inflammatory property of IMMA. However, different from the previous reports showing that IMMA can increase endocannabinoids without interfering with arachidonic acid metabolism, treatment with IMMA failed to elevate the endogenous levels of AEA and 2-AG, but significantly reduced the production of prostaglandin E2 (PGE2). Furthermore, the mRNA expression of enzymes involved in PGE2 production, COX-2 and prostaglandin E synthase 2 in the ipsilateral sciatic nerve was also suppressed by IMMA treatment. Taken together, these results suggested that IMMA might exert anti-nociceptive effects through multiple mechanisms which include, but are not limited to, CB2 receptor activation and reduced PGE2 production.
Providing a New Energy Management Approach in Smart Homes Using the 6lowpan Protocol

Smart home energy management system (HEMS) can be considered as an optimal system of providing energy management services for effective monitoring and management of energy production, power storage and power consumption. HEMS play an important role in improving energy efficiency, reliability and energy conservation. One of the key challenges in the smart home is to reduce power consumption, to decrease the power costs and to increase energy efficiency. Accordingly, in this study a new smart home energy management approach is presented in which a wireless network is used based on 6LOWPAN protocol in the system of communication and smart home area network for communicating between home appliances and intelligent energy management system. The proposed scheduling algorithm for monitoring and controlling of electrical appliances at home is responsible for scheduling and planning the convenient start time for them. Also in this algorithm, the start time can be shifted from peak hour to off-peak ones if the consumable and unnecessary electrical appliances want to start at peak hours and the energy storage system not to be able to provide enough power to get started by using the electrical appliances. In this study, the contiki operating system and Cooja simulator are used to simulate and implement the algorithm. The simulation result indicates that the proposed scheduling algorithm can reduce power consumption and power costs which in turn it can increase the energy efficiency.
Surface Reconstruction Based on the Modified Gauss Formula

In this article, we introduce a surface reconstruction method that has excellent performance despite nonuniformly distributed, noisy, and sparse data. We reconstruct the surface by estimating an implicit function and then obtain a triangle mesh by extracting an iso-surface. Our implicit function takes advantage of both the indicator function and the signed distance function. The implicit function is dominated by the indicator function at the regions away from the surface and is approximated (up to scaling) by the signed distance function near the surface. On one hand, the implicit function is well defined over the entire space for the extracted iso-surface to remain near the underlying true surface. On the other hand, a smooth iso-surface can be extracted using the marching cubes algorithm with simple linear interpolations due to the properties of the signed distance function. Moreover, our implicit function can be estimated directly from an explicit integral formula without solving any linear system. An approach called disk integration is also incorporated to improve the accuracy of the implicit function. Our method can be parallelized with small overhead and shows compelling performance in a GPU version by implementing this direct and simple approach. We apply our method to synthetic and real-world scanned data to demonstrate the accuracy, noise resilience, and efficiency of this method. The performance of the proposed method is also compared with several state-of-the-art methods.
Homomorphic Encryption for Compressed Sensing in Wireless Sensor Networks

Sensors are tiny devices with limited power (generally a battery for the full sensor lifetime) and reduced computing resources and memory. WSN are deployed for many applications like environment monitoring and health monitoring. In order to reduce the energy consumption and increase the WSN life, adapted network protocols like routing and data aggregation are used. The scheduling of the communications also allow better management of active/idle status and hence optimize the energy consumption. Classical data compression and decompression algorithms are not adapted to WSN as requiring high memory and computation resources. Compressed sensing is an emerging method with practical results in fields like medical imaging. The adoption of compressed sensing in WSN is attracting many research efforts [1234]. Ensuring data security is a key challenge for sensitive applications like health and military. We have used Homomorphic encryption to protect data confidentiality with low energy consumption.
Improving Word Embedding Coverage in Less-Resourced Languages Through Multi-Linguality and Cross-Linguality

In the era of deep learning-based systems, efficient input representation is one of the primary requisites in solving various problems related to Natural Language Processing (NLP), data mining, text mining, and the like. Absence of adequate representation for an input introduces the problem of data sparsity, and it poses a great challenge to solve the underlying problem. The problem is more intensified with resource-poor languages due to the absence of a sufficiently large corpus required to train a word embedding model. In this work, we propose an effective method to improve the word embedding coverage in less-resourced languages by leveraging bilingual word embeddings learned from different corpora. We train and evaluate deep Long Short Term Memory (LSTM)-based architecture and show the effectiveness of the proposed approach for two aspect-level sentiment analysis tasks (i.e., aspect term extraction and sentiment classification). The neural network architecture is further assisted by hand-crafted features for prediction. We apply the proposed model in two experimental setups: multi-lingual and cross-lingual. Experimental results show the effectiveness of the proposed approach against the state-of-the-art methods.
Winter Is Coming: A Socio-Environmental Monitoring and Spatiotemporal Modelling Approach for Better Understanding a Respiratory Disease

Chronic Obstructive Pulmonary Disease is a progressive lung disease affecting the respiratory function of every sixth New Zealander and over 300 million people worldwide. In this paper, we explored how the combination of social, demographical and environmental conditions (represented by increased winter air pollution) affected hospital admissions due to COPD in an urban area of Christchurch (NZ). We juxtaposed the hospitalisation data with dynamic air pollution data and census data to investigate the spatiotemporal patterns of hospital admissions. Spatial analysis identified high-risk health hot spots both overall and season specific, exhibiting higher rates in winter months not solely due to air pollution, but rather as a result of its combination with other factors that initiate deterioration of breathing, increasing impairments and lead to the hospitalisation of COPD patients. From this we found that socioeconomic deprivation and air pollution, followed by the age and ethnicity structure contribute the most to the increased winter hospital admissions. This research shows the continued importance of including both individual (composition) and area level (composition) factors when examining and analysing disease patterns.
Q-Table compression for reinforcement learning

Abstract Reinforcement learning (RL) algorithms are often used to compute agents capable of acting in environments without prior knowledge of the environment dynamics. However, these algorithms struggle to converge in environments with large branching factors and their large resulting state-spaces. In this work, we develop an approach to compress the number of entries in a Q-value table using a deep auto-encoder. We develop a set of techniques to mitigate the large branching factor problem. We present the application of such techniques in the scenario of a real-time strategy (RTS) game, where both state space and branching factor are a problem. We empirically evaluate an implementation of the technique to control agents in an RTS game scenario where classical RL fails and provide a number of possible avenues of further work on this problem.
1, 2, 3, Many - Perceptual Integration of Motif Repetitions

It is generally assumed that the initial integration of visual information is limited in its spatial extent. Of particular interest is the extent to which image symmetries are detected and integrated. Here we studied the spatial extent of visual integration in textures constructed from wallpaper symmetry groups. Using tools from statistical physics, we obtained images ranging from symmetric ones to completely random ones, whereas the textural elements were of the same quality. Results show that the psychometric curves for 3 × 3 motif repetitions are similar to those of images having more repetitions, whereas an equivalent physical scaling of the images does not alter the performance.
Furthest-Pair-Based Decision Trees: Experimental Results on Big Data Classification

Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space consumed and accuracy. The major enhancement of the FPBST includes converting the resultant BST to a decision tree, in order to remove the need for the slow K-nearest neighbors (KNN), and to obtain a smaller tree, which is useful for memory usage, speeding both training and testing phases and increasing the classification accuracy. The proposed decision trees are based on calculating the probabilities of each class at each node using various methods; these probabilities are then used by the testing phase to classify an unseen example. The experimental results on some (small, intermediate and big) machine learning datasets show the efficiency of the proposed methods, in terms of space, speed and accuracy compared to the FPBST, which shows a great potential for further enhancements of the proposed methods to be used in practice.
Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm

Positioning is an essential element in most Internet of Things (IoT) applications. Global Positioning System (GPS) chips have high cost and power consumption, making it unsuitable for long-range (LoRa) and low-power IoT devices. Alternatively, low-power wide-area (LPWA) signals can be used for simultaneous positioning and communication. We summarize previous studies related to LoRa signal-based positioning systems, including those addressing proximity, a path loss model, time difference of arrival (TDoA), and fingerprint positioning methods. We propose a LoRa signal-based positioning method that uses a fingerprint algorithm instead of a received signal strength indicator (RSSI) proximity or TDoA method. The main objective of this study was to evaluate the accuracy and usability of the fingerprint algorithm for large areas in the real world. We estimated the locations using probabilistic means based on three different algorithms that use interpolated fingerprint RSSI maps. The average accuracy of the three proposed algorithms in our experiments was 28.8 m. Our method also reduced the battery consumption significantly compared with that of existing GPS-based positioning methods.
Novel CMOS MO-CFDITA based fully electronically controlled square/triangular wave generator with adjustable duty cycle

This study presents a novel square/triangular wave generator based on multiple-output current follower differential input transconductance amplifier (MO-CFDITA) with reduced complexity in terms of transistors. The proposed generator comprises of single MO-CFDITA and one grounded capacitor only which makes the proposed generator circuit suitable to integrated circuit implementation. The proposed generator provides output square wave in current mode and output triangular wave in voltage mode. The amplitude of current-mode square wave is electronically and independently tunable via bias current. The DC level of the triangular wave is also electronically adjustable. The proposed generator has dual-slope operation and its duty cycle is adjustable with the help of DC current source over a range of 5–95%. The proposed generator consumes a power of 1.45 mW only and it is usable up to 50 MHz which is quite good operating frequency range. To examine the performance of the proposed generator, the cadence VIRTUOSO simulation results have been depicted. Additionally, the simulation results and performance parameters of complementary metal–oxide–semiconductor (CMOS) MO-CFDITA are included. The practicality of the proposed generator is verified through the experimental results.
Cyclicity in EL-Hypergroups

In the algebra of single-valued structures, cyclicity is one of the fundamental properties of groups. Therefore, it is natural to study it also in the algebra of multivalued structures (algebraic hyperstructure theory). However, when one considers the nature of generalizing this property, at least two (or rather three) approaches seem natural. Historically, all of these had been introduced and studied by 1990. However, since most of the results had originally been published in journals without proper international impact and later—without the possibility to include proper background and context-synthetized in books, the current way of treating the concept of cyclicity in the algebraic hyperstructure theory is often rather confusing. Therefore, we start our paper with a rather long introduction giving an overview and motivation of existing approaches to the cyclicity in algebraic hyperstructures. In the second part of our paper, we relate these to E L -hyperstructures, a broad class of algebraic hyperstructures constructed from (pre)ordered (semi)groups, which were defined and started to be studied much later than sources discussed in the introduction were published.
UO_IRO: Linguistic Informed Deep-learning Model for Irony Detection

English. This paper describes our UO IRO system developed for participating in the shared task IronITA, organized within EVALITA: 2018 Workshop. Our approach is based on a deep learning model informed with linguistic knowledge. Specifically, a Convolutional (CNN) and Long Short Term Memory (LSTM) neural network are ensembled, also, the model is informed with linguistics information incorporated through its second to last hidden layer. Results achieved by our system are encouraged, however a more finetuned hyper-parameters setting is required for improving the model’s effectiveness. Italiano. Questo articolo descrive il nostro sistema UO IRO, sviluppato per la partecipazione allo shared task IronITA, presso EVALITA 2018. Il nostro approccio si basa su un modello di deep learning con conoscenza linguistica. In particolare: una Convolutional Neural Network (CNN) e una Long Short Term Memory Neural Network (LSTM). Inoltre, il modello è arricchito da conoscenza linguistica, incorporata nel penultimo hidden layer del modello. Sebbene sia necessario un miglioramento a grana fine dei parametri per migliorare le prestazioni del modello, i risultati ottenuti sono incorag-
Overview of the Evalita 2018 Task on Automatic Misogyny Identification (AMI)

English. Automatic Misogyny Identiﬁ-cation (AMI) is a new shared task proposed for the ﬁrst time at the Evalita 2018 evaluation campaign. The AMI challenge, based on both Italian and English tweets, is distinguished into two subtasks, i.e. Subtask A on misogyny identiﬁcation and Subtask B about misogynistic behaviour categorization and target classiﬁ-cation. Regarding the Italian language, we have received a total of 13 runs for Sub-task A and 11 runs for Subtask B. Concerning the English language, we received 26 submissions for Subtask A and 23 runs for Subtask B. The participating systems have been distinguished according to the language, counting 6 teams for Italian and 10 teams for English. We present here an overview of the AMI shared task, the datasets, the evaluation methodology, the results obtained by the participants and a discussion of the methodology adopted by the teams. Finally, we draw some conclusions and discuss future work.
The role of culture in language and cognition

This paper provides an overview of some recent research on how culture is causally implicated in the understanding of human cognition. In particular I review studies on the influence of culture on short-term memory, visual perception, grammar, numerical cognition, and language evolution. I also provide a list of desiderata for research methodologies on the connections between culture and cognition and a direction for future research.
Fuzzy AHP Application for Supporting Contractors' Bidding Decision

This paper proposes the author’s model based on the Fuzzy Analytic Hierarchy Process (FAHP) to improve the efficiency of contractor bidding decisions. The essence of the AHP method is to make pairwise comparisons of available options against all evaluation criteria. The results of these comparisons are recorded in a square matrix in which symmetrical elements are reciprocal. In the expert opinion, a 9-step, bipolar verbal scale was used so that the symmetry of the response was maintained. For contractors from countries where the tendering system is commonly used, the choice of the right tender in which to participate influences their image, financial condition, and their aspiration to succeed. The bid/no bid decision depends on numerous factors associated with the company itself, the environment, and the project concerning the tender. When facing tough competition, contractors search for a solution which increases their chances of winning the tender. The proposed model was based on factors selected by Polish contractors. The original element of the model involves 4 original criteria and 15 sub-criteria for the assessment of investment decision projects to the selection of the most advantageous contract, i.e., the contractor’s participation in the bid. For verbal evaluations describing the criteria, symmetric triangular fuzzy numbers were assigned. The authors performed an extended analysis method combined with FAHP in the model. Fuzzy evaluations underwent elaborate analysis, the aim of which was to specify the synthetic priority weights for each criterion. As a result of the application of the method, to prove that the model works, an example from the Polish construction market was presented in which a bid/no bid decision about four possible tenders was to be taken. Despite the considered example applying to Polish conditions, the proposed model can be used also in other countries. The authors’ rationale is to produce new and more flexible methodologies in order to realistically model a variety of concrete decision problems.
Hybrid Synchronization Problem of a Class of Chaotic Systems by an Universal Control Method

The hybrid synchronization problem of a class of chaotic systems is investigated in this paper. Firstly, the existence of hybrid synchronization problems in such systems is proved theoretically by a proposed necessary and sufficient condition. That is, the hybrid synchronization problem is equivalent to solve a group of nonlinear algebraic equations about α . It is interesting that one value of α indicates one type of synchronization. Secondly, all solutions for the hybrid synchronization problem are obtained by finding solutions of all the above equations about α . Thirdly, an universal control method is proposed to realize such hybrid synchronization problems. Finally, illustrative examples are provided to verify the validity and effectiveness of the obtained results.