Low-quality Version Research Articles

The Possibilities of Modern Client-Centered and Experiential Psychotherapy

In Ukraine, important processes are underway to identify those areas of psychotherapy that deserve official recognition by law. The official recognition of psychotherapeutic methods and techniques is associated with the fact that they are recognized as clinically and statistically effective. Such psychotherapy is reimbursed by insurance and is actively promoted in training courses, often "at the expense of other methods of psychotherapy." The psychotherapeutic procedure itself is becoming more and more standardized and impersonal, conforming to a protocol. The fate of client-centered and experiential psychotherapy (PCE-therapy) is not so easy in the world: evidence of its effectiveness is not reflected in the NICE (National Center for Collaboration in Mental Health) guidelines for depression and schizophrenia, which to some extent determine health policy. Therefore, there are widespread prejudices about the ineffectiveness of this type of therapy: it is not effective for severe mental illness, crisis states, specific phobias and traumas, behavioral problems, for those clients who need a directive approach, for assessing and diagnosing clients, etc. The article presents specific studies of the effectiveness of client-centered and experiential psychotherapy for specific symptoms (anxiety, depression, schizophrenia, borderline personality disorders, and some somatic disorders), as well as data from a meta-analysis of the effectiveness of client-centered and experiential therapy. PCE-therapy proved to be highly effective according to the criteria: "before-after" treatment; 2) "treatment-no treatment"; 3) "comparison with other types of psychotherapy" - the data obtained are generally statistically and clinically equivalent in terms of effectiveness to other methods of psychotherapy. PCE therapy proved to be the most effective in the following cases: interpersonal problems, self-harming behavior, coping with chronic somatic diseases, psychosis. It turned out that cognitive behavioral therapy (CBT) has mixed benefits compared to PCE therapy. However, Elliott et al. note that, firstly, these "studies were mostly performed by CBT therapists" and, secondly, "low-quality versions of PCE therapy were used as comparison conditions". The most effective methods of PCE therapy were EFT and PCT. A list of approaches included in PCA-therapy is given, and their general characteristics are given.

Optimal strategy of in-game items with conspicuous consumption: whether to provide the grinding version?

In the virtual world, whether or not to spend money on in-game items distinguishes paying players from non-paying players. Due to the existence of conspicuous psychology, paying players will greatly increase their conspicuous utility after purchasing an item in addition to the utility of the item itself. In this case, whether providing different versions of items can bring greater revenue to the game company is a question worth investigating. In this study, two analytical models considering conspicuous intensity are developed to compare the optimal pricing strategy of the game company providing the single-version item or dual-version items. The single-version item can only be purchased, while a relatively low-quality version that can be obtained by grinding is provided in the dual-version strategy. Grinding means using time in games to get items instead of spending in games. The results suggest that it is more profitable for companies to offer dual-version items when conspicuous intensity is strong. Game companies can also adjust the time needed to acquire the grinding version item and the quality gap between the two versions to achieve greater revenue. The research contributes to providing a theoretical basis and decision support for game companies to decide whether to provide different versions of in-game items.

Twice Mixing: A rank learning based quality assessment approach for underwater image enhancement

Objectively and accurately evaluating underwater images generated by different enhancement algorithms is an essential issue, which however is still largely under-explored. In this paper, we present a novel rank learning guided no-reference quality assessment method to evaluate different underwater image enhancement (UIE) algorithms. It is also the first work that utilizes deep learning approaches to address this problem. Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image and its low-quality version. Twice Mixing is trained based on an elaborately formulated self-supervision mechanism. Specifically, before each iteration, we randomly generate two mixing ratios which will be utilized for both generating virtual images and guiding the network training. In the test phase, a single branch of the network is extracted to predict the quality rankings of different UIE outputs. Additionally, to train our network, we construct a new dataset that contains over 2200 raw underwater images and their high/low-quality versions. Twice Mixing is evaluated on both synthetic and real-world datasets. Experimental results show that the proposed approach outperforms the previous methods significantly.

Remote sensing image recovery via enhanced residual learning and dual-luminance scheme

Low-quality (LQ) images will seriously affect the performance of the information processing system in the field of remote sensing. To recover a high-quality (HQ) remote sensing image from the LQ version, the remote sensing image recovery (RSIR) methods are widely studied. In this paper, we propose a novel enhanced residual convolutional neural network (ERCNN) with dual-luminance scheme (DLS) for RSIR. Our network mainly focuses on learning the residual features to better deal with the high-frequency recovery. Specifically, ERCNN is mainly constructed by enhanced residual groups (ERGs), and ERG is further constructed by enhanced residual blocks (ERBs). Two strategies are used in ERB: proposing an enhanced feature flow module for improving the flowability of feature information with a reasonable parameter number; utilizing the feature attention module to enhance the ability of distinguish learning across feature maps. Furthermore, we introduce two kinds of reversible transformation layers into our network for a larger receptive field and a lower memory burden. Moreover, we propose DLS to further boost the RSIR ability of ERCNN, leading to the boosting version BERCNN. Experimental results on two typical RSIR problems demonstrate the superiority of our method over other RSIR methods.

Private Labels in Marketplaces

This paper investigates the implications of vertical integration with private labels in the marketplace model opposed to the classic wholesale model. Differently from classic retailers, on a marketplace firms set end-consumer prices and the intermediary collects fees. When introducing a lower-quality version of a product, a marketplace owner does not have an incentive to increase the cost of the outside seller and foreclose him. In order to protect revenues from the seller channel, a marketplace owner overprices his product, compared to a retailer or stand-alone monopolist, and decreases the fee. I demonstrate that offering a lower quality is indeed optimal for both marketplace owner and classic retailer, with the former differentiating more from the seller's offering. This harms the seller less, but improves the consumer surplus less compared to a retailer.

Optimal service versioning for dating platforms

In this study, we examine the versioning strategy for two-sided dating platforms. We assume a monopoly dating platform facing two sides of users (male and female) with different levels of willingness to pay across and within the same side. The platform knows the user type distribution on each side but does not know the exact types of the individual users. The platform needs to decide whether or when to offer different versions of its services to the different types of users. The main findings are as follows: (1) the platform should always serve both types of users on each side; (2) the platform should choose to conduct versioning when the proportion of low-type users is small and/or their taste for quality is markedly different from that of high-type users, and choose not to do so otherwise; (3) when versioning is optimal, the platform should degrade the low-quality version as much as possible; and (4) versioning is never socially optimal. The necessary condition for versioning to improve social welfare is that high-type users benefit more from quality improvement than low-type users suffer from quality reduction.

Deep‐network based method for joint image deblocking and super‐resolution

Many pieces of research have been conducted on image-restoration techniques to recover high-quality images from their low-quality versions, but they usually aim to handle a single degraded factor. However, captured images usually suffer from various degradation factors, such as low resolution and compression distortion, in the procedures of image acquisition, compression, and transmission simultaneously. Ignoring the correlation of different degraded factors may result in the limited efficiency of the existing image-restoration methods for captured images. A joint deep-network-based image-restoration algorithm is proposed to establish a restoration framework for image deblocking and super-resolution. The proposed convolutional neural network is made up of two stages. A deblocking network is constructed with two cascade deblocking subnets first, then, super-resolution is performed by a very deep network with skipping links. Cascading these two stages forms a novel deep network. An end-to-end training scheme is developed, which makes the two stages be trained jointly so as to achieve better performance. Intensive evaluations have been conducted to measure the performance of the authors’ method both in general images and face images. Experimental results on several datasets demonstrate that the proposed method outperforms other state-of-the-art methods, in terms of both subjective and objective performances.

Upgrading Heterogeneous Consumers

We study how a durable-goods monopolist upgrades heterogeneous consumers in an infinite horizon model of quality growth. While unable to charge consumers based on their purchase histories, the monopolist may engage in both inter- and intra-temporal price discriminations: charge different prices for goods of the same quality over time and offer different buyback prices for earlier, lower quality versions. First, we consider that there is a frictionless secondhand market such that consumers are anonymous. The monopolist always keeps high-value consumers abreast of the latest development. Moreover, when consumers are similar, the monopolist also upgrades low-value consumers at the same pace as the high-value ones. Otherwise, the monopolist completely ignores low-value consumers and focuses only on the high-value ones. Second, we consider where there is no secondhand market and consumers are semi-anonymous. If consumers and the monopolist are impatient, the outcome is the same as in an anonymous market. Otherwise, besides the efficient pooling equilibrium, there are three interesting inefficient upgrade patterns where high-value consumers always upgrade every period: the low-value consumers (1) keep a constant quality difference from the high-value ones, (2) upgrade in cycles but below the quality frontier, and (3) upgrade to the frontier in cycles.

MULTI-TIER PAY WHAT YOU WANT: COMBINING ENDOGENOUS AND SECOND-DEGREE PRICE DISCRIMINATION

In the last decade, several sellers have experimented with participative pricing, i.e., pricing mechanisms in which the buyers directly influence the price of a transaction. Pay What You Want (PWYW) is the most rigorous participative pricing model. The buyer can set any price, including zero, and the seller cannot refuse to serve the buyer for that price (Kim, Natter, & Spann, 2009). For the seller, PWYW can be advantageous if the buyers pay differentiated prices that are higher than posted prices. However, the profitability of the pricing model is limited to specific situations (Gerpott, 2017). We introduce a modification of PWYW labeled Multi-Tier Pay What You Want (MTPWYW). In MTPWYW, the seller offers different quality levels of a good. The lower-quality version is available under pure PWYW conditions. The higher-quality versions are only available to customers who pay at least a predefined threshold price. Nonetheless, customers can still pay more than the threshold price for the higher-quality good. Therefore, MTPWYW allows the seller to exclude customers who pay low prices for the higher-quality good. We run a field experiment comparing MTPWYW to PWYW by setting up a waffle stand with two quality levels: the basic version consisted of plain waffles only, the higher-quality version included waffles with toppings. In the case of PWYW, the buyers could choose any price they wanted to for both quality levels. In the case of MTPWYW, waffles with toppings were only available after paying at least the threshold price. The results indicate that MTPWYW can be more profitable than PWYW and that it can achieve two forms of price discrimination: endogenous price discrimination and second-degree price discrimination (i.e., self-selection into different quality levels).

Transcoding Based Video Caching Systems: Model and Algorithm

The explosive demand of online video watching brings huge bandwidth pressure to cellular networks. Efficient video caching is critical for providing high‐quality streaming Video‐on‐Demand (VoD) services to satisfy the rapid increasing demands of online video watching from mobile users. Traditional caching algorithms typically treat individual video files separately and they tend to keep the most popular video files in cache. However, in reality, one video typically corresponds to multiple different files (versions) with different sizes and also different video resolutions. Thus, caching of such files for one video leads to a lot of redundancy since one version of a video can be utilized to produce other versions of the video by using certain video coding techniques. Recently, fog computing pushes computing power to edge of network to reduce distance between service provider and users. In this paper, we take advantage of fog computing and deploy cache system at network edge. Specifically, we study transcoding based video caching in cellular networks where cache servers are deployed at the edge of cellular network for providing improved quality of online VoD services to mobile users. By using transcoding, a cached video can be used to convert to different low‐quality versions of the video as needed by different users in real time. We first formulate the transcoding based caching problem as integer linear programming problem. Then we propose a Transcoding based Caching Algorithm (TCA), which iteratively finds the placement leading to the maximal delay gain among all possible choices. We deduce the computational complexity of TCA. Simulation results demonstrate that TCA significantly outperforms traditional greedy caching algorithm with a decrease of up to 40% in terms of average delivery delay.

Exploiting textures for better action recognition in low-quality videos

Human action recognition is an increasingly matured field of study in the recent years, owing to its widespread use in various applications. A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied. However, the general problem of video quality—a realistic issue in the face of low-cost surveillance infrastructure and mobile devices, has not been systematically investigated from various aspects. In this paper, we address the problem of action recognition in low-quality videos from a myriad of perspectives: spatial and temporal downsampling, video compression, and the presence of motion blurring and compression artifacts. To increase the resilience of feature representation in these type of videos, we propose to use textural features to complement classical shape and motion features. Extensive results were carried out on low-quality versions of three publicly available datasets: KTH, UCF-YouTube, HMDB. Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.

Recipients’ Assessment of Journalistic Quality

Many studies and their findings are available concerning most aspects of online user comments and their effects. But there are few data regarding the question of whether user comments have an impact on an audience’s assessment of journalistic quality, and regarding the question of how user comments and the actual journalistic quality interact. Therefore, two experimental studies were conducted. In both experiments, the quality of a journalistic product and the valence of user comments were manipulated. The results indicate that, overall, high-quality journalistic products were evaluated as (slightly) better than low-quality versions. Furthermore, users evaluated the quality of journalistic products with positive user comments as better than the quality of identical products with negative comments. Considering that, in reality, user comments are predominantly critical, it is worrying that these comments have rather negative effects on the evaluation of journalistic work.

Selling through outlets: The impact of quality, product development risk, and market awareness

We consider a monopolist manufacturer of a luxury good who currently sells a product through a retail store. The manufacturer must decide whether to also offer this product, at a lower quality level, through a factory outlet store. We study how this decision depends on the relative qualities of the products offered on the two channels, as well as the manufacturer's ability to develop successful new products. Our multi-period model captures both risky new product development and the impact of outlet sales on the manufacturer's brand awareness. We find that the manufacturer's optimal strategy will be one of three options: expand into the existing outlet channel by introducing a low-quality version of the product, do not expand into the outlet channel, or expand into the outlet channel only when new product development is successful. The “wait and see” strategy becomes optimal when the fixed cost associated with expanding into the outlet channel is moderate and the likelihood of successful new product development is low. In this case, expanding into the outlet channel is only preferred when new product development is successful because successful development helps counteract the negative impact of outlet sales on the perceived exclusivity of the brand. Finally, we demonstrate that the manufacturer's optimal strategy is dependent on the level of product differentiation provided by the outlet and the impact of brand awareness on brand quality.

Selling information products: Sale channel selection and versioning strategy with network externality

The purpose of this paper is to provide a quantitative analysis of selling information products, aiming to determine the best sale channel and versioning strategy in the presence of network externality. An information product supply chain consisting of an information supplier, a retailer, and consumers is considered. In this supply chain, a Stackelberg game exists between the supplier and retailer, in which the supplier is the leader and the retailer is the follower. This paper analyzes the decision behaviors of the supplier, the retailer, and consumers in selling or buying information products. In our tractable analyses, consumer valuation for the product is uniformly distributed, and network externality intensity ranges from 0 to 1. A sale channel model is developed to maximize the supplier׳s profit based on the fixed-fee policy in the presence of the network externality. By comparing different channels, we determined the best sale channel and versioning strategy for a supplier. Our main findings include: A supplier prefers the versioning strategy when network externality exists in the market, whereas the single-version strategy is more preferable for an information product when network externality does not exist in the market. When two versions are released in a market with network externality, the best sale channel strategy is that the high-quality version is distributed through the direct sale channel and the low-quality version is sold through the retailing channel.

Human gait identification from extremely low‐quality videos: an enhanced classifier ensemble method

Nowadays, surveillance cameras are widely installed in public places for security and law enforcement, but the video quality may be low because of the limited transmission bandwidth and storage capacity. In this study, the authors proposed a gait recognition method for extremely low-quality videos, which have a frame-rate at one frame per second (1 fps) and resolution of 32 × 22 pixels. Different from popular temporal reconstruction-based methods, the proposed method uses the average gait image (AGI) over the whole sequence as the appearance-based feature description. Based on the AGI description, the authors employed a large number of weak classifiers to reduce the generalisation errors. The performance can be further improved by incorporating the model-based information into the classifier ensemble. The authors found that the performance improvement is directly proportional to the average disagreement level of weak classifiers (i.e. diversity), which can be increased by using the model-based information. The authors evaluated the proposed method on both indoor and outdoor databases (i.e. the low-quality versions of OU-ISIR-D and USF databases), and the results suggest that our method is more general and effective than other state-of-the-art algorithms.

Increasing Quality Sequence: When Is It an Optimal Product Introduction Strategy?

In this paper, we analyze the optimal introduction timing of a seller’s products targeted at segments that differ in their willingness to pay for quality. Past studies suggest that an introduction sequence of a high-quality product followed by a lower-quality version of the product may mitigate the cannibalization effects of the low-quality product on profits from the high-quality product. We show that if customers who value quality more possess an outside option such as a substitute product, as may be the case with replacement buyers, a seller may find it optimal to follow a low-quality product with a higher-quality one, the latter being targeted at replacement buyers. Furthermore, the ability of the seller to commit to future qualities accentuates the sequential increase in quality in the presence of such buyers. Thus, we show that conditions other than uncertainty or technological improvements occurring over time may justify a seller adopting a strategy of sequentially increasing quality. This paper was accepted by J. Miguel Villas-Boas, marketing.

Chaotic dynamics in the software market with network externalities

In this study, we built discrete-time dynamic systems to investigate the chaotic dynamics of a software market with network externalities. Monopoly and duopoly systems are considered separately. The chaotic features of both monopoly and duopoly models are verified through numerical simulations, including bifurcation diagrams, maximal Lyapunov exponent, and strange attractors. In the monopoly model, the strategy of distributing low-quality versions is evaluated. The impacts of decision parameters on the dynamics of the system are also investigated. Meanwhile, the Ott, Grebogi, and Yorke method is adopted to control chaos in the monopoly model by making moderate changes in the market evolution speed of the low-quality version software. In the duopoly model, the system dynamics are illustrated numerically. Three different cases are considered in evaluating decisions of firms regarding distribution or non-distribution of the low-quality version software. Furthermore, the compatibility parameter is examined with regards to its impacts on the dynamics of the duopoly system.

Line Extension Asymmetry: Higher Quality Line Extensions Help, Lower Quality Extensions Do Only a Little Harm

Abstract "Managers often extend brands to different quality levels. Adding lower priced variants is a particularly popular option for fighting the growing number of retailer brands. Such a move may increase sales, but it also risks diluting brand image. This study examines such line extensions by testing middle-quality brands that offer higher or lower quality line extensions. According to the results, the adverse effects of brands’ lower quality versions seem to be overestimated. Higher quality line extensions improved overall brand perception and evaluation far more than lower quality extensions damaged them. This asymmetry prevailed in multiple product classes and for various dimensions of brand evaluation such as brand attitude, brand expertise or brand innovation. In general, consumers seem to prefer broader product lines. Even if lower quality extensions reduced brand prestige, there was hardly any effect in the overall evaluation. The negative quality association was tempered by increased perceived brand innovation and positive variety effects. However, lower quality extensions are not harmless in every case. Managers are well advised to consider all branding options and to analyze possible effects, not only on the brand but also on the individual product. "

Optimal versioning strategy for information products with behavior-based utility function of heterogeneous customers

This paper reconsiders two fundamental assumptions (i.e., on the quality of information product and the self-selection behavior of customers) that decide the optimality of versioning strategy (or vertically differentiated product line) for information products. The quality of an information product is clarified in terms of functional and nonfunctional features. The customers׳ behavior of self-selection among multiple versions of an information product is examined, and the disability of linear valuation function for exactly capturing customers׳ valuation on information products is clarified. The required quality is introduced to depict a customer׳s requirement on the quality of an information product, and a behavior-based utility function is thus defined, where a customer has a marginal decrease (different from the constant marginal in linear valuation function) on the valuation of versions with quality levels higher than the required, but the valuation of a customer diminishes quickly on versions with quality levels lower than the required. Then, a bilevel programming model is built to represent the task for optimizing the strategy of versioning an information product, with the monopolist as the leader and all customers as followers. Optimal quality levels and prices for multiple versions are obtained for a given number of versions. To deal with the nonlinearity and multimodality of this model due to correlated decision variables, a steady-state evolutionary algorithm, hybridized with the local search method (called the hybrid steady-state evolutionary algorithms), is developed to improve the global optimality of versioning schemes. Numerical experiments are conducted on the bilevel programming model with specific parameterization by using the hybrid algorithm. Experimental results verify the optimality of multi-version strategy and reveal various facets of its property. With the behavior-based valuation function of heterogeneous customers, the multi-version strategy is superior to the one-version scheme. The total profit increases logarithmically with the maximal number of versions, which means that the introduction of a new lower-quality version contributes less to the total profit of a monopolist. Furthermore, when a lower quality level version is offered to the market, higher quality versions are priced higher than they were in previous versioning schemes, indicating that a multi-version scheme can make more detailed segmentation of the market. Therefore, a monopolist is able to gain greater revenue via price discrimination on heterogeneous customers through vertically differentiating information products.

Experience Information Goods: 'Version-to-Upgrade'

In contrast to “search goods” whose true quality can be determined before inspection, we examine informa- tion goods that are “experience goods” — goods whose true quality can only be determined through use. We analyze a “version-to-upgrade” strategy where a monopolist generates vertically differentiated versions as bridges that lead consumers to experience the goods so that they can assess their true quality, and then provide upgrades to consumers that initially purchase lower quality versions. Adopting a two-stage model, we find that if consumers have homogeneous expectations about quality before experience, then the version-to-upgrade strategy involves upgrading all the consumers that in the first stage purchased the low quality version. In this way, consumers that upgrade effectively pay a tax for learning. When consumers have heterogeneous expectations about quality before experience, if consumers are pessimistic, then the version-to-upgrade strategy still drives all consumers to upgrade. However, if consumers are optimistic, then, the version-to-upgrade strategy may induce only some of the consumers that initially purchased the low quality version to upgrade. As profits from upgrades increase, the monopolist sets the quality of the low quality version to the lowest quality that can feasibly reveal the true quality, justifying the use of trial or demonstration versions.