Reviewerly/exHarmony huggingface dataset:
Ekonomik Özgürlük ve İçsel Ekonomik Büyüme

The relation between economic freedoms and economic performance is very crucial to determine economic growth and to the development policies of a country. Economic development and growth are the ultimate goal for all developed and emerging economies. Economic developed countries have been aiming to sustain their current situation, while developing countries apply policies that will provide economic growth and development. Convergence hypothesis which is the one of the main inference of Solow growth model states that, in a close economy, real income difference across countries tends to decrease because of the diminishing rate of return of capital. The concept of "economic freedoms" is one of the driving elements of economic growth and development. This study tests empirically economical freedom foster macroeconomic growth in an endogenous growth model. Panel data is used in covering 159 countries for the period of 1995-2014 via nonlinear least square methods. Findings suggest that there is significant relation between economical freedom and per capita growth. JEL Classification: E2, O43, O47, D02, E10
Change or Not: A Simple Approach for Plug and Play Language Models on Sentiment Control

Text generation with sentiment control is difficult without fine-tuning or modifying the model architecture. Plug and Play Language Model (PPLM) utilizes an external sentiment classifier to update the hidden states of GPT-2 at each time step. It does not change the parameters but achieves competitive performance. However, fluency is impaired due to the instability of the hidden states. Moreover, the classifier is not strong because of the way it is trained with partial texts, hence it is difficult to guide the generation in the process. To solve the above problems, in this paper, we first propose a fixed threshold method based on the Valence-Arousal-Dominance (VAD) lexicon to decide whether to change a word, which keeps the fluency of the original LM to the greatest extent. Furthermore, for the improvement of sentiment alignment, we propose a dynamic threshold method that utilizes VAD-based loss to make the threshold dynamic. Experiments demonstrate that our methods outperform the baseline with a great margin significantly both on fluency and sentiment accuracy.
FracBits: Mixed Precision Quantization via Fractional Bit-Widths

Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. During the optimization, the bit-width of each layer / kernel in the model is at a fractional status of two consecutive bit-widths which can be adjusted gradually. With a differentiable regularization term, the resource constraints can be met during the quantization-aware training which results in an optimized mixed precision model. Further, our method can be naturally combined with channel pruning for better computation cost allocation. Our final models achieve comparable or better performance than previous quantization methods with mixed precision on MobilenetV1/V2, ResNet18 under different resource constraints on ImageNet dataset.
Squeeze-and-Threshold Based Quantization for Low-Precision Neural Networks

In this work, we propose a method based on attention to quantize convolutional neural networks (CNNs) to run on low-precision (binary and multi-bit). Intuitively, high-quality pictures are very conducive to distinguishing objects. However, even in low-quality black-and-white photos (analogous to low-precision), various features can also be well distinguished and the content is easily understood. Based on this intuition, we introduce an attention-based block called squeeze-and-threshold (ST) to adjust different features to different ranges and learn the best threshold to distinguish (quantize) them. Furthermore, to eliminate the extra calculations caused by the ST block in the inference process, we propose a momentum-based method to learn the inference threshold during the training stage. Additionally, with the help of ST block, our quantization approach is faster and takes less than half the training epochs of prior multi-bit quantization works. The experimental results on different datasets and networks show the versatility of our method and demonstrate state-of-the-art performance.
Time-Independent Planning for Multiple Moving Agents

Typical Multi-agent Path Finding (MAPF) solvers assume that agents move synchronously, thus neglecting the reality gap in timing assumptions, e.g., delays caused by an imperfect execution of asynchronous moves. So far, two policies enforce a robust execution of MAPF plans taken as input: either by forcing agents to synchronize or by executing plans while preserving temporal dependencies. This paper proposes an alternative approach, called time-independent planning, which is both online and distributed. We represent reality as a transition system that changes configurations according to atomic actions of agents, and use it to generate a time-independent schedule. Empirical results in a simulated environment with stochastic delays of agents' moves support the validity of our proposal.
LSQB

We introduce LSQB, a new large-scale subgraph query benchmark. LSQB tests the performance of database management systems on an important class of subgraph queries overlooked by existing benchmarks. Matching a labelled structural graph pattern, referred to as subgraph matching, is the focus of LSQB. In relational terms, the benchmark tests DBMSs' join performance as a choke-point since subgraph matching is equivalent to multi-way joins between base Vertex and base Edge tables on ID attributes. The benchmark focuses on read-heavy workloads by relying on global queries which have been ignored by prior benchmarks. Global queries, also referred to as unseeded queries, are a type of queries that are only constrained by labels on the query vertices and edges. LSQB contains a total of nine queries and leverages the LDBC social network data generator for scalability. The benchmark gained both academic and industrial interest and is used internally by 5+ different vendors.
Non-Autoregressive Coarse-to-Fine Video Captioning

It is encouraged to see that progress has been made to bridge videos and natural language. However, mainstream video captioning methods suffer from slow inference speed due to the sequential manner of autoregressive decoding, and prefer generating generic descriptions due to the insufficient training of visual words (e.g., nouns and verbs) and inadequate decoding paradigm. In this paper, we propose a non-autoregressive decoding based model with a coarse-to-fine captioning procedure to alleviate these defects. In implementations, we employ a bi-directional self-attention based network as our language model for achieving inference speedup, based on which we decompose the captioning procedure into two stages, where the model has different focuses. Specifically, given that visual words determine the semantic correctness of captions, we design a mechanism of generating visual words to not only promote the training of scene-related words but also capture relevant details from videos to construct a coarse-grained sentence "template". Thereafter, we devise dedicated decoding algorithms that fill in the "template" with suitable words and modify inappropriate phrasing via iterative refinement to obtain a fine-grained description. Extensive experiments on two mainstream video captioning benchmarks, i.e., MSVD and MSR-VTT, demonstrate that our approach achieves state-of-the-art performance, generates diverse descriptions, and obtains high inference efficiency. Our code is available at https://github.com/yangbang18/Non-Autoregressive-Video-Captioning.
Robust Decisions for Heterogeneous Agents via Certainty Equivalents

We study the problem of a planner who resolves risk-return trade-offs -- like financial investment decisions -- on behalf of a collective of agents with heterogeneous risk preferences. The planner's objective is a two-stage utility functional where an outer utility function is applied to the distribution of the agents' certainty equivalents from a given decision. Assuming lognormal risks and heterogeneous power utility preferences for the agents, we characterize optimal behavior in a setting where the planner can let each agent choose between different options from a fixed menu of possible decisions, leading to a grouping of the agents by risk preferences. These optimal decision menus are derived first for the case where the planner knows the distribution of preferences exactly and then for a case where he faces uncertainty about this distribution, only having access to upper and lower bounds on agents' relative risk aversion. Finally, we provide tight bounds on the welfare loss from offering a finite menu of choices rather than fully personalized decisions.
Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

Abstract The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights.
Unmanned Aerial Vehicle Mid-Air Collision Detection and Resolution Using Avoidance Maps

Collision avoidance for fixed-wing unmanned aerial vehicles (UAVs), using a novel methodology of avoidance map, is addressed in this work. Avoidance map is defined as a mapping in the control input space of a pair of UAVs, which partitions the control space into avoidance and collision regions, respectively. The choice of maneuvers to execute successful avoidance can be effectively obtained by using the avoidance map. The duration of control application is determined by a simple method that is time optimal and computationally efficient. Different versions of the map portraying the evolution of the concept are also given. Precision control, defined as a gradual reduction in control, is discussed to reduce the control input demand for the entire duration of avoidance. The concept is extended to multiple UAVs, and the additional requirement of the UAVs to return to nominal trajectory after avoidance, using Dubins paths, is also addressed. Finally, simulation results using realistic UAV models are considered, and some needed modifications to the basic avoidance strategy, made necessary by realistic maneuver models, are discussed. Several illustrative simulation results are given to support the applicability of the proposed method.
Distributed Multi-robot Collision Avoidance Using the Voronoi-based Method

Abstract Collision avoidance is a fundamental problem in multi-robot motion planning, enabling robots to coordinate in a safe and efficient manner. In this paper, we introduce several best-known distributed collision avoidance algorithms and present the Voronoi-based method in detail, which assigns optimal dominance regions for robots using the Voronoi diagram, constrains robot motions within the buffered Voronoi diagram, and follows right-hand rule when deadlock happens during navigating to the goals. A series of simulations are conducted to demonstrate the efficacy of collision avoidance guarantee under robot localization uncertainty while avoiding deadlock using our presented algorithm. We claim that such a method is superior to most of the existing distribution methods in a known environment in efficiency of implementation.
One for More: Selecting Generalizable Samples for Generalizable ReID Model

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch. It will inevitably cause the model to over-fit the data in the dominant position (e.g., head data in imbalanced class, easy samples or noisy samples). The latest resampling methods address the issue by designing specific criterion to select specific samples that trains the model generalize more on certain type of data (e.g., hard samples, tail data), which is not adaptive to the inconsistent real world ReID data distributions. Therefore, instead of simply presuming on what samples are generalizable, this paper proposes a one-for-more training objective that directly takes the generalization ability of selected samples as a loss function and learn a sampler to automatically select generalizable samples. More importantly, our proposed one-for-more based sampler can be seamlessly integrated into the ReID training framework which is able to simultaneously train ReID models and the sampler in an end-to-end fashion. The experimental results show that our method can effectively improve the ReID model training and boost the performance of ReID models.
Optimal Control of Closed Quantum Systems via B-Splines with Carrier Waves

We consider the optimal control problem of determining electromagnetic pulses for implementing logical gates in a closed quantum system, where the Hamiltonian models the dynamics of coupled superconducting qudits. The quantum state is governed by Schrödinger's equation, which we formulate in terms of the real and imaginary parts of the state vector and solve by the Störmer--Verlet scheme, which is a symplectic partitioned Runge--Kutta method. A novel parameterization of the control functions based on B-splines with carrier waves is introduced. The carrier waves are used to trigger the resonant frequencies in the system Hamiltonian, and the B-spline functions specify their amplitude and phase. This approach allows the number of control parameters to be independent of, and significantly smaller than, the number of time steps for integrating Schrödinger's equation. We present numerical examples of how the proposed technique can be combined with an interior point limited memory BFGS algorithm for realizing quantum gates and generalize our approach to calculate risk-neutral controls that are resilient to noise in the Hamiltonian model. The proposed method is also shown to compare favorably with QuTiP/pulse_optim and Grape-TensorFlow.
Adversarial Attack Mitigation Approaches Using RRAM-Neuromorphic Architectures

The rising trend and advancements in machine learning has resulted into its numerous applications in the field of computer vision, pattern recognition to providing security to hardware devices. Eventhough the proven achievements showcased by advancement in machine learning, one can exploit the vulnerabilities in those techniques by feeding adversaries. Adversarial samples are generated by well crafting and adding perturbations to the normal input samples. There exists majority of the software based adversarial attacks and defenses. In this paper, we demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. We also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.
Web Browser Forensic Tools: Autopsy, BHE and Net Analysis

Information and communication technology (ICT) are becoming an integral part of everyone´s lives which affects all sectors of human activity.Nowadays, the level of computer crimes rises and it is alarming, for such, digital forensic investigation plays a vital role in tackling computer-related crimes such as cyberbullying, fraud, cyber intrusion, hacking an et cetera.The main goal of digital forensic investigation is to preserve any evidence found in its most original form and to make sure that the evidence is not tempered.Digital forensic investigators use log files to extract, analyse and present a report based on the criminal activities found on the web browsers, such log files include history, cache, download and cookies.This survey paper evaluates the features of the three selected web browsers forensic tools namely; Browser History Examiner, Autopsy and NetAnalysis, and make comparative analysis between them.The findings of the evaluation based on the features of the selected tools shows that Autopsy is the best forensic tool among them.Therefore, in this paper, Autopsy has been recommended fora digital investigator or examiner to use as their forensic tool among others.
Sui requisiti di una buona comunicazione nel libro di Kamila Miłkowska-Samul (S)cortesia e social network. Opportunità e rischi del dibattito pubblico su Facebook

On the requirements of good communication in the book by Kamila Miłkowska-Samul (S)cortesia e social network. Opportunità e rischi del dibattito pubblico su Facebook The article aims to discuss the latest book by Kamila Miłkowska-Samul, entitled (S)cortesia e social network. Opportunità e rischi del dibattito pubblico su Facebook, published in 2019. It is the second time that the author takes up the subject of the language of Italian politics, this time in a pragmalinguistic and communicative perspective. The interpretation key to which the analysis of numerous examples of politicians’ statements on Facebook was subordinated is the theory of politeness (or impoliteness). The reader will also find an in-depth study of individual theoretical component issues: concerning the language of the Internet, politics, and the already mentioned theory of politeness and related sociopragmalinguistic issues.
Performance analysis of Svelte and Angular applications

The aim of this article is to check if the Svelte-based client part of a web application is more effective than the standard Angular approach. The article presents a comparison of page components rendering times on the basis of two test applications prepared in both frameworks. For the performance tests, scenarios were prepared in which the times of adding and removing a different number of page components were examined. Application tests were performed using the Selenium Webdriver package. The research results clearly showed that the new approach used for DOM manipulation (Svelte v.3.0) is more efficient than the standard solution used in Angular applications (v.10.2).
Isles of Emotion: Emotionally Expressive Social Virtual Spaces for Reflection and Communication

It is common not be fully aware of our experience of emotions, and sometimes it is hard to communicate our emotions to others. Using design strategies derived from ambiguity and performativity, we came up with the concept of Isles of Emotion, customizable abstract virtual spaces that represent emotions. Considering remote communication as one of the potential use cases, we assessed this concept as an emotional reflection and communication tool via user studies conducted with 6 pairs of closely related people who have lived apart in the near past. During the studies participants created their own emotion islands using VR tools and visited their own and their pair’s islands. We contribute to the existing literature by sharing resulting design insights on emotionally expressive social virtual spaces, commenting on the level of abstraction from and connections to the real-world, sharing and collaboration concerning the islands and their customizability.
Video‐Based Rendering of Dynamic Stationary Environments from Unsynchronized Inputs

Abstract Image‐Based Rendering allows users to easily capture a scene using a single camera and then navigate freely with realistic results. However, the resulting renderings are completely static, and dynamic effects – such as fire, waterfalls or small waves – cannot be reproduced. We tackle the challenging problem of enabling free‐viewpoint navigation including such stationary dynamic effects, but still maintaining the simplicity of casual capture. Using a single camera – instead of previous complex synchronized multi‐camera setups – means that we have unsynchronized videos of the dynamic effect from multiple views, making it hard to blend them when synthesizing novel views. We present a solution that allows smooth free‐viewpoint video‐based rendering (VBR) of such scenes using temporal Laplacian pyramid decomposition video, enabling spatio‐temporal blending. For effects such as fire and waterfalls, that are semi‐transparent and occupy 3D space, we first estimate their spatial volume. This allows us to create per‐video geometries and alpha‐matte videos that we can blend using our frequency‐dependent method. We also extend Laplacian blending to the temporal dimension to remove additional temporal seams. We show results on scenes containing fire, waterfalls or rippling waves at the seaside, bringing these scenes to life.
Topological Semantic Mapping by Consolidation of Deep Visual Features

Many works in the recent literature introduce semantic mapping methods that use CNNs (Convolutional Neural Networks) to recognize semantic properties in images. The types of properties (eg.: room size, place category, and objects) and their classes (eg.: kitchen and bathroom, for place category) are usually predefined and restricted to a specific task. Thus, all the visual data acquired and processed during the construction of the maps are lost and only the recognized semantic properties remain on the maps. In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN (GoogLeNet), from 2D images captured in multiple views of the environment as the robot operates, to create, through averages, consolidated representations of the visual features acquired in the regions covered by each topological node. These representations allow flexible recognition of semantic properties of the regions and use in other visual tasks. Experiments with a real-world indoor dataset showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results.
E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy

Convolutional neural networks (CNNs) have been increasingly deployed to edgedevices. Hence, many efforts have been made towards efficient CNN inference onresource-constrained platforms. This paper attempts to explore an orthogonaldirection: how to conduct more energy-efficient training of CNNs, so as toenable on-device training? We strive to reduce the energy cost during training,by dropping unnecessary computations, from three complementary levels:stochastic mini-batch dropping on the data level; selective layer update on themodel level; and sign prediction for low-cost, low-precision back-propagation,on the algorithm level. Extensive simulations and ablation studies, with realenergy measurements from an FPGA board, confirm the superiority of our proposedstrategies and demonstrate remarkable energy savings for training. For example,when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of>90% and >60%, while incurring a top-1 accuracy loss of only about 2% and 1.2%,respectively. When training ResNet-110 on CIFAR-100, an over 84% trainingenergy saving is achieved without degrading inference accuracy.