Miracle-dz/UniFAR-FTUs huggingface dataset:
The Method of Using Production Rules in Neural Network Recognition of Emotions by Facial Geometry

The article is devoted to the improvement of neural network means of recognition of emotions on human geometry, which are defined for use in information systems of general purpose. It is shown that modern means of emotional recognition are based on the usual networks of critical disadvantage, because there is a lack of accuracy of recognition under the influence of purchased, characteristic of general-purpose information systems. It is determined that the above remarks relate to the turning of the face and the size of the image. A typical approach to overcoming this disadvantage through training is unacceptable for all protection options that are inappropriate for reasons of duration and compilation of the required training sample. It is proposed to increase the accuracy of recognition by submitting an expert data model to the neural network. An appropriate method for representing expert knowledge is developed. A feature of the method is the use of productive rules and the PNN neural network. Experimental verification of the developed solutions has been carried out. The obtained results allow to increase the efficiency of the termination and disclosure of the set of age networks, the characteristics of which are not presented in the registered statistical data.
Routing in optical transport networks with deep reinforcement learning

Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and automated control problems. In the context of networking, there is a growing trend in the research community to apply DRL algorithms to optimization problems such as routing. However, existing proposals fail to achieve good results, often under-performing traditional routing techniques. We argue that the reason behind this poor performance is that they use straightforward representations of networks. In this paper, we propose a DRL-based solution for routing in optical transport networks (OTNs). Contrary to previous works, we propose a more elaborate representation of the network state that reduces the level of knowledge abstraction required for DRL agents and easily captures the singularities of network topologies. Our evaluation results show that using our novel representation, DRL agents achieve better performance and learn how to route traffic in OTNs significantly faster compared to state-of-the-art representations. Additionally, we reverse engineered the routing strategy learned by our DRL agent, and as a result, we found a routing algorithm that outperforms well-known traditional routing heuristics.
Use of virtual private networks (VPN) and proxy servers: Impact of personality and demographics

Internet security and privacy is a trending issue. Different tools are being employed by different users to stay anonymous and safe. The research focused on impact of Big Five Inventory personality traits on use of VPN and proxy servers. The research was conducted in the Czech Republic. The respondents were 478 university students. Gender, age, and type of student’s job were used as control variables. With regards to the results, conscientiousness, openness to experience, gender, and type of student’s job influence the preference. It is less conscientious, more open to experience, male, and working full time in the field they study that use VPN and proxy servers.
Stein’s Lemma for Classical-Quantum Channels

It is well known that for the discrimination of classical and quantum channels in the finite, non-asymptotic regime, adaptive strategies can give an advantage over non-adaptive strategies. However, Hayashi [IEEE Trans. Inf. Theory 55(8), 3807 (2009)] showed that in the asymptotic regime, the exponential error rate for the discrimination of classical channels is not improved in the adaptive setting. We show that, for the discrimination of classical-quantum channels, adaptive strategies do not lead to an asymptotic advantage. As our main result, this establishes Stein’s lemma for classical-quantum channels. Our proofs are based on the concept of amortized distinguishability of channels, which we analyse using entropy inequalities.
A Generalization of the Blackburn-Etzion Construction for Private Information Retrieval Array Codes

Private Information Retrieval (PIR) array codes were introduced by Fazeli et al. (2015) to reduce the storage overhead in designing PIR protocols. Blackburn and Etzion (2017) introduced the (virtual server) rate to quantify the storage overhead of the codes, and when s > 2 (here, $\frac{1}{s}$ is the proportion of the database storing in one server), they gave a general construction of PIR array codes with the highest rate known so far. In this paper, we generalize their construction and reduce the number of servers, while maintaining the rate. In order to give PIR array codes with significantly fewer servers, we also construct classes of codes with a smaller rate $\frac{s}{{2s - 1}}$.
Understanding the Effects of Gamification and Juiciness on Players

Gamification is widely applied to increase user engagement and motivation, but empirical studies on effectiveness are inconclusive, and often limited to the integration of tangible elements such as leaderboards or badges. In this paper, we report findings from a study with 36 participants that uses the lens of Self-Determination Theory to compare traditional gamification elements, and the concept of juiciness (the provision of abundant audiovisual feedback) in the VR simulation Predator! Results show that gamification and juiciness improve user experience, but that only juiciness fulfills all basic psychological needs that facilitate intrinsic motivation when applied in nongaming settings. User preferences favour the combination of both approaches, however, neither improved performance, and there is evidence of juicy elements influencing user behaviour. We discuss implications of these findings for the integration of gamification, reflect on the role of both approaches in the context of feedback, and outline challenges and opportunities for further research.
Not So Different Games: An exploration of five years of student game designers

Sixty percent of Americans play video games daily, with 45% of U.S. gamers identifying as female. Based on a 2017 survey by the International Game Developers Association, only 22% of the game industry is composed of women, while 81% of all respondents rated diversity in the workplace as important. This begs the question, how can we promote a more gender diverse gaming industry? Academic settings, such as courses focused on game design, offer the opportunity for women and men to experiment, learn and play, while earning self-confidence and a sense that they equally belong to the field. Diversity of content in gaming is in demand and both genders’ perspectives are needed. Understanding how game design approaches may or may not differ across genders can help inform pedagogical and curricular choices within academia. In this study, we observe students’ design preferences in the making of video games over a five year period, and analyze how student game designers’ choices with regard to elements such as setting and game-play fit into the conversation of promoting inclusive game design.We examine whether different groups of student game designers create different games. In particular, our study focuses on teams that are composed of at least half female designers and those with less than half female team members. The results from two analyses that examined genre, setting, mood, aesthetic and others, showed no statistically significant difference among the two groups, with the exception of one criterion related to the presence of identifiable enemies. This study points to the fact that shared foci across genders are prevalent, indicating fruitful avenues for future game development that may be broadly appealing and inclusive.
An Efficient Algorithm for Capacity-Approaching Noisy Adaptive Group Testing

In this paper, we consider the group testing problem with adaptive test designs and noisy outcomes. We propose a computationally efficient four-stage procedure with components including random binning, identification of bins containing defective items, 1-sparse recovery via channel codes, and a "clean-up" step to correct any errors from the earlier stages. We prove that the asymptotic required number of tests comes very close to the best known information-theoretic achievability bound (which is based on computationally intractable decoding), and approaches a capacity-based converse bound in the low-sparsity regime.
Asynchronous Radio Duty Cycling for Green IoT: State of the Art and Future Perspectives

Energy efficiency is paramount for IoT. One of the culprits for energy consumption in a typical sensor or actuator -- core components of IoT -- is the radio. Thus, duty cycling (i.e., turning the radio off when it is not needed) becomes essential. While wireless communication standards commonly include provisions for duty cycling, they are usually based on synchronization, which may be expensive in terms of hardware, software, or control communication overhead. Asynchronous methods have been extensively studied in the context of wireless sensor networks and thus are valuable tools for green IoT deployments. In this article, we provide a review of existing methods, from seminal works to recent proposals. We categorize and compare them qualitatively and quantitatively. Finally, we discuss recent trends in the area and suggest topics for future research.
On the Construction of Optimal Embedding Problems for Delay-Sensitive Service Function Chains

Network Function Virtualization (NFV) with minimum-delay service function chains will be a key enabling technology for next generation mobile networks, such as 5G. In this paper, we present a new approach to generate problem instances for the cost optimized delay sensitive Virtualized Network Function (VNF) placement and routing problem, where we know by construction the optimal solution for the given objective function. Our approach produces problem instances, which can be used to test and benchmark heuristic algorithms against. We then implement an Affinity based simulated annealing (ABSA) heuristic approach for cost optimized delay aware placement of VNFs along with a set of greedy approaches. We evaluate the implemented approaches against the optimal solution by generating several problem instances using our proposed method.
Reduce Transmission Delay for Caching-Aided Two-Layer Networks

In this paper, we consider a two-layer caching-aided network, where a single server consisting of a library of N files connects with multiple relays, each equipped with a cache memory of M1 files and each relay connects with a distinct set of users, each equipped with a cache memory of M2 files. We design a caching scheme that exploits the spared transmission time resource by constructing a concurrent transmission between the two layers. It is shown that the caching scheme is order optimal and achieves an additive parallel gain compared to the previously known caching scheme. Also, we show that for the two-relay case, if each relay’s caching size M1 equals to 0.382N, our scheme achieves the optimal delay as M1 = N, implying that increasing the relay’s cache size will not always reduce the transmission delay.
Sample-Measurement Tradeoff in Support Recovery Under a Subgaussian Prior

Data samples from ℝd with common support of size k are accessed through m linear projections per sample. In the measurement-starved regime of m < k, how many samples are needed to recover the common support? We answer this question for a generative model with independent samples drawn from a subgaussian prior. We show that n = Θ((k2/m2) log(k(d − k))) samples are necessary and sufficient to exactly recover the support. Our proposed sample-optimal estimator has a closed-form expression and has computational complexity of O(dnm).
Day-ahead Scheduling in a Local Electricity Market

Local electricity markets offer new trading opportunities for existing and emerging actors in the energy sector. In this study, a mathematical model is created for the hourly scheduling of a day-ahead local electricity market. The local electricity market has local producers and consumers and a connection with a retailer. The local resources considered are solar PV production, and an energy storage system. Therefore, the local electricity market has local resources, the inflexible and flexible residential loads, and the connection to the distribution network. This connection is used for buying energy from the wholesale market through a retailer. For evaluating the feasibility of the scheduling in a local energy market, an optimization model is proposed in this study to maximize the operational profits from the local electricity market, which is managed by an energy service company (ESCO). There are revenues from selling the energy to all residential loads, while the costs come from buying the electricity and managing all the local resources. A case study illustrates the energy profiles of all the resources managed, which have an impact on revenues and costs of the local market. From the case study, we draw some practical conclusions about the impact of local resources on the local electricity market.
Ergodic Spatial Nulling for Achieving Interference Free Rates

It is shown that a receiver equipped with two antennas may null an arbitrary large number of spatial directions to any desired level, while maintaining the interference-free signal-to-noise ratio, by judiciously adjusting the distance between the antenna elements. The main theoretical result builds on ergodic theory. The practicality of the scheme for systems operating at a moderate signal-to-noise ratio is demonstrated for a scenario where each transmitter is equipped with a single antenna and each receiver has two antenna elements, the separation of which can be arbitrarily set. As an example, for a five-user planar line-of-sight interference channel, with the directions of users being uniformly distributed, at a signal-to-noise ratio of 10 dB, a near interference-free average transmission rate is achievable. This amounts to roughly doubling the average rate attained by non-naive time-division multiple access.
Coded MapReduce Schemes Based on Placement Delivery Array

The coded MapReduce framework introduced in [1] gives a method to tradeoff extra computation for reduced communication, in-order to speedup operations for which communication is the bottleneck. In [2], it has been demonstrated that reducing the number of subfiles required in coded MapReduce at the cost of a slightly higher communication load is beneficial for certain problems. The placement delivery array (PDA), introduced in [3], is a structure used to develop coded caching schemes with small sub-packetization. In the present paper, we use PDA to come up with a method to construct coded MapReduce schemes which require smaller number of subfiles. This method gives a way to tradeoff between the number of subfiles and the communication required. We also address the problem of mitigating the impact of slow servers at the map phase on the reduce operations of normal servers.
A Combined Deep Learning Model for the Scene Classification of High-Resolution Remote Sensing Image

Deep learning now plays an important role in solving complex problems in computer vision fields. The highly challenging high-resolution remote sensing image scene classification problem can also be solved using deep learning methods. The most commonly used method of deep learning is the convolutional neural network model. In this letter, based on deep learning, a combined model named Inception-long short-term memory (LSTM) is proposed. First, we combine the deep learning feature extracted from the pretrained Inception-V3 model with a hand-crafted feature: the GIST feature. The different features are then combined and input into the batch normalization (BN) layer. Second, the BN layer plays the role of the bridge to combine the InceptionV3 model with the LSTM model, which features a softmax classifier. The LSTM model is used to analyze the features and classify the different high-resolution remote sensing scene images. The proposed model, as a whole, can be uniformly trained. Three different datasets—the NWPU-RESISC45 dataset, the UC Merced dataset, and the SIRI-WHU dataset—were used to verify the effectiveness of the proposed model. The results show that the proposed Inception-LSTM model shows an outstanding performance in the scene classification task.
An Almost Perfectly Predictable Process with No Optimal Predictor

A novel kind of a negative result is presented for the problem of computable prediction. A non-stationary binary stochastic process is constructed for which almost surely no effective method of prediction achieves the infimum of prediction errors defined as the normalized Hamming distance between the sequence of predictions and the realization of the process. Yet it is shown that this process may be effectively predicted almost surely up to an arbitrarily small error since the infimum of prediction errors is zero.
Inhibiting Mycobacterium tuberculosis DosRST Signaling by Targeting Response Regulator DNA Binding and Sensor Kinase Heme

Mycobacterium tuberculosis (Mtb) possesses a two-component regulatory system, DosRST, that enables Mtb to sense host immune cues and establish a state of nonreplicating persistence (NRP). NRP bacteria are tolerant to several antimycobacterial drugs in vitro and are thought to play a role in the long course of tuberculosis therapy. Previously, we reported the discovery of six novel chemical inhibitors of DosRST, named HC101A–106A, from a whole cell, reporter-based phenotypic high throughput screen. Here, we report functional and mechanism of action studies of HC104A and HC106A. RNaseq transcriptional profiling shows that the compounds downregulate genes of the DosRST regulon. Both compounds reduce hypoxia-induced triacylglycerol synthesis by ∼50%. HC106A inhibits Mtb survival during hypoxia-induced NRP; however, HC104A did not inhibit survival during NRP. An electrophoretic mobility assay shows that HC104A inhibits DosR DNA binding in a dose-dependent manner, indicating that HC104A may function by directly targeting DosR. In contrast, UV–visible spectroscopy studies suggest HC106A directly targets the sensor kinase heme, via a mechanism that is distinct from the oxidation and alkylation of heme previously observed with artemisinin (HC101A). Synergistic interactions were observed when DosRST inhibitors were examined in pairwise combinations with the strongest potentiation observed between artemisinin paired with HC102A, HC103A, or HC106A. Our data collectively show that the DosRST pathway can be inhibited by multiple distinct mechanisms.
Risk of Myocardial Infarction Among New Users of Calcium Supplements Alone or Combined With Vitamin D: A Population‐Based Case‐Control Study

A population‐based case‐control study was conducted to evaluate the risk of acute myocardial infarction among new users of calcium supplements either in monotherapy (CaM) or in combination with vitamin D (CaD). A total of 23,025 cases and 114,851 controls randomly sampled from the underlying cohort and matched with cases by age, sex, and index date were included. New users of CaM and CaD were categorized as current users, recent users, past users, and nonusers. We computed adjusted odds ratios (AORs) and their 95% confidence intervals (CIs) among current users as compared with nonusers through a conditional logistic regression. No increased risk was associated with CaM overall (59 cases (0.26%) and 273 controls (0.24%); AOR = 0.80; 95% CI 0.59–1.09), nor was it found in any of the conditions examined. Instead, the use of CaD was associated with a decreased risk (275 cases (1.19%) and 1,160 controls (1.45%); AOR = 0.78; 95% CI 0.67–0.90), dose and duration‐dependent, and particularly evident in patients with a high cardiovascular risk (AOR = 0.59; 95% CI 0.43–0.81).
Final Analysis of DeCOG-SLT Trial: No Survival Benefit for Complete Lymph Node Dissection in Patients With Melanoma With Positive Sentinel Node.

PURPOSE We have previously reported on the 3-year results of the phase III German Dermatologic Cooperative Oncology Group trial (DeCOG; ClinicalTrials.gov identifier: NCT02434107) comparing distant metastasis-free survival (DMFS), recurrence-free survival (RFS), and overall survival (OS) in patients with positive sentinel lymph-node biopsy who were randomly assigned to complete lymph node dissection (CLND) or observation. Here, we report the final analysis with 72 months of median follow up. PATIENTS AND METHODS The multicenter randomized phase III trial included patients with cutaneous melanoma of the trunk and extremities who were randomly assigned (1:1) to undergo CLND or observation. DMFS was analyzed as the primary end point, and RFS, OS, and recurrences in the regional lymph node basin were secondary end points. The analysis was by intention to treat. Disease and survival information were collected quarterly. RESULTS From January 2006 to December 2014, 5,547 patients were screened to identify 1,256 with metastases in the sentinel lymph node (SLN). Of these, 483 (39%) were included: 241 in the observation arm and 242 in the CLND arm. In the final analysis, median follow up was 72 months (interquartile range, 67-77 months). No significant treatment-related difference was seen in the 5-year DMFS between the observation and CLND arms (67.6% v 64.9%, respectively; hazard ratio [HR], 1.08; P = .87). The 5-year RFS and OS also showed no difference (HR, 1.01 and 0.99, respectively). Grade 3 and 4 adverse effects occurred in 32 patients (13%) in the CLND arm; lymphedema (n = 20) and delayed wound healing (n = 5) were most common and no serious adverse events were reported. CONCLUSION The final results of the German Dermatologic Cooperative Oncology Group trial with a median follow up of 72 months showed higher event rates, but similar HRs compared with those at the 3-year analysis. These results confirm that immediate CLND in SLN-positive patients is not superior to observation in terms of DMFS, RFS, or OS and support not recommending CLND in patients with SLN metastasis.
The role of lipid metabolism in aging, lifespan regulation, and age‐related disease

An emerging body of data suggests that lipid metabolism has an important role to play in the aging process. Indeed, a plethora of dietary, pharmacological, genetic, and surgical lipid‐related interventions extend lifespan in nematodes, fruit flies, mice, and rats. For example, the impairment of genes involved in ceramide and sphingolipid synthesis extends lifespan in both worms and flies. The overexpression of fatty acid amide hydrolase or lysosomal lipase prolongs life in Caenorhabditis elegans, while the overexpression of diacylglycerol lipase enhances longevity in both C. elegans and Drosophila melanogaster. The surgical removal of adipose tissue extends lifespan in rats, and increased expression of apolipoprotein D enhances survival in both flies and mice. Mouse lifespan can be additionally extended by the genetic deletion of diacylglycerol acyltransferase 1, treatment with the steroid 17‐α‐estradiol, or a ketogenic diet. Moreover, deletion of the phospholipase A2 receptor improves various healthspan parameters in a progeria mouse model. Genome‐wide association studies have found several lipid‐related variants to be associated with human aging. For example, the epsilon 2 and epsilon 4 alleles of apolipoprotein E are associated with extreme longevity and late‐onset neurodegenerative disease, respectively. In humans, blood triglyceride levels tend to increase, while blood lysophosphatidylcholine levels tend to decrease with age. Specific sphingolipid and phospholipid blood profiles have also been shown to change with age and are associated with exceptional human longevity. These data suggest that lipid‐related interventions may improve human healthspan and that blood lipids likely represent a rich source of human aging biomarkers.