The market is already crammed with open-world multiplayer survival games, but this post-apocalyptic epic adds cosmic horror to the mix. Its huge world is crammed with grotesque Lovecraftian monsters – including a living bus like the benevolent catbus in My Neighbour Totoro, but just horrible. You use an axe, dodges and a pistol to interrupt your powerful enemies' attacks and gain the upper hand – but this animated world is much brighter and less bleak, inspired by its developers' antipodean surroundings. I have never seen anything quite like this forthcoming game from Capcom: you control a warrior protecting a priestess as she slowly dances her way through monster-infested Japanese mountain scenes, purifying her surroundings as you go. During the day you rescue people and station your troops; at night you attack the monsters, hoping to keep her safe.

Human-robot physical interaction contains crucial information for optimizing user experience, enhancing robot performance, and objectively assessing user adaptation. This study introduces a new method to evaluate human-robot co-adaptation in lower limb exoskeletons by analyzing muscle activity and interaction torque as a two-dimensional random variable. We introduce the Interaction Portrait (IP), which visualizes this variable's distribution in polar coordinates. We applied this metric to compare a recent torque controller (HTC) based on kinematic state feedback and a novel feedforward controller (AMTC) with online learning, proposed herein, against a time-based controller (TBC) during treadmill walking at varying speeds. Compared to TBC, both HTC and AMTC significantly lower users' normalized oxygen uptake, suggesting enhanced user-exoskeleton coordination. IP analysis reveals this improvement stems from two distinct co-adaptation strategies, unidentifiable by traditional muscle activity or interaction torque analyses alone. HTC encourages users to yield control to the exoskeleton, decreasing muscular effort but increasing interaction torque, as the exoskeleton compensates for user dynamics. Conversely, AMTC promotes user engagement through increased muscular effort and reduced interaction torques, aligning it more closely with rehabilitation and gait training applications. IP phase evolution provides insight into each user's interaction strategy development, showcasing IP analysis's potential in comparing and designing novel controllers to optimize human-robot interaction in wearable robots.

Roadway reconfiguration is a crucial aspect of transportation planning, aiming to enhance traffic flow, reduce congestion, and improve overall road network performance with existing infrastructure and resources. This paper presents a novel roadway reconfiguration technique by integrating optimization based Brute Force search approach and decision support framework to rank various roadway configurations for better performance. The proposed framework incorporates a multi-criteria decision analysis (MCDA) approach, combining input from generated scenarios during the optimization process. By utilizing data from optimization, the model identifies total betweenness centrality (TBC), system travel time (STT), and total link traffic flow (TLTF) as the most influential decision variables. The developed framework leverages graph theory to model the transportation network topology and apply network science metrics as well as stochastic user equilibrium traffic assignment to assess the impact of each roadway configuration on the overall network performance. To rank the roadway configurations, the framework employs machine learning algorithms, such as ridge regression, to determine the optimal weights for each criterion (i.e., TBC, STT, TLTF). Moreover, the network-based analysis ensures that the selected configurations not only optimize individual roadway segments but also enhance system-level efficiency, which is particularly helpful as the increasing frequency and intensity of natural disasters and other disruptive events underscore the critical need for resilient transportation networks. By integrating multi-criteria decision analysis, machine learning, and network science metrics, the proposed framework would enable transportation planners to make informed and data-driven decisions, leading to more sustainable, efficient, and resilient roadway configurations.

Natural Language Processing technology has advanced vastly in the past decade. Text processing has been successfully applied to a wide variety of domains. In this paper, we propose a novel framework, Text Based Classification(TBC), that uses state of the art text processing techniques to solve classification tasks on tabular data. We provide a set of controlled experiments where we present the benefits of using this approach against other classification methods. Experimental results on several data sets also show that this framework achieves comparable performance to that of several state of the art models in accuracy, precision and recall of predicted classes.

Convolutional Neural Networks (CNN) have shown impressive performance in computer vision, natural language processing, and many other applications, but they exhibit high computations and substantial memory requirements. To address these limitations, especially in resource-constrained devices, the use of cloud computing for CNNs is becoming more popular. This comes with privacy and latency concerns that have motivated the designers to develop embedded hardware accelerators for CNNs. However, designing a specialized accelerator increases the time-to-market and cost of production. Therefore, to reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties, which is going to be more prevalent in futuristic artificial intelligence of things (AIoT) systems. These AIoT systems anticipate horizontal collaboration among different resource-constrained AIoT node devices, where CNN layers are partitioned and these devices collaboratively compute complex CNN tasks Therefore, there is a dire need to explore this attack surface for designing secure embedded hardware accelerators for CNNs. Towards this goal, in this paper, we exploited this attack surface to propose an HT-based attack called FeSHI. This attack exploits the statistical distribution i.e., Gaussian distribution, of the layer-by-layer feature maps of the CNN to design two triggers for stealthy HT with a very low probability of triggering. To illustrate the effectiveness of the proposed attack, we deployed the LeNet and LeNet-3D on PYNQ to classify the MNIST and CIFAR-10 datasets, respectively, and tested FeSHI. The experimental results show that FeSHI utilizes up to 2% extra LUTs, and the overall resource overhead is less than 1% compared to the original designs

The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Most previous works focused on solving MCDS problem in graphs with relatively small size, mainly due to the complexity of maintaining connectivity. This paper explores techniques for solving MCDS problem in massive real-world graphs with wide practical importance. Firstly, we propose a local greedy construction method with reasoning rule called 1hopReason. Secondly and most importantly, a hybrid dynamic connectivity maintenance method (HDC+) is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and its previous counterpart. Thirdly, we adopt a two-level vertex selection heuristic with a newly proposed scoring function called chronosafety to make the algorithm more considerate when selecting vertices. We design a new local search algorithm called FastCDS based on the three ideas. Experiments show that FastCDS significantly outperforms five state-of-the-art MCDS algorithms on both massive graphs and classic benchmarks.

Last year's ensemble Avengers game was a bit of a disappointment, so DC fans will be hoping for better from this action game set in the Batman universe. As Nightwing, Batgirl, Robin and Red Hood, you push back against the ever-encroaching criminal forces of Gotham. It looks moody and ultra-stylish. A stylish art-deco shooter about a time-travelling assassin caught in a time loop. Stuck in an endlessly repeating island party, he must take out eight of its guests – but the really interesting part is that another assassin, controlled by another player, is trying to stop him doing exactly that.

From the developers of Assassin's Creed: Odyssey, it sends the player out to face creatures drawn from the gnarliest Greek legends and rescue the gods. A platform game for anyone who thinks video games are too easy these days, Ori draws its play inspiration from classics such as Mario, Mega Man and Metroid, but its looks are bang up to date. Guiding a spirit through an intensely beautiful forest, you'll come up against puzzles and obstacles that challenge both your mind and your reflexes. Let's hope they're still sharp. Originally released in 1997, Final Fantasy VII is one of the most beloved and acclaimed role-playing titles of all time.

Every new year brings with it the promise of astonishing video games, but what does 2017 have in store? Will this be a vintage year? From returning legends to innovative new projects, there's an impressive amount of fascinating stuff on the way – and we've tried to cram in everything, including big budget sequels, unexpected offshoots and tiny independent projects. If we've somehow overlooked your highlight of the coming year, let us know in the comments section! Described as a magical realist adventure, 29 is set within a single flat (actually owned by the game's development team), and follows the lives of its inhabitants as they prepare to move out and move on with their lives.